Fully Homomorphic Encryption (FHE) is a powerful privacy-preserving technology enabling secure computation on encrypted data, but it suffers from substantial performance overheads. Running FHE efficiently typically requires developing dedicated FHE accelerators, which can be costly and inflexible. Instead of pursuing entirely new accelerators, this paper explores an alternative paradigm: augmenting generic computing devices with a modular FHE-specific hardware extension unit (HEU) to create an efficient FHE engine. To make this paradigm viable, we propose three key innovations: (1) recognizing that some FHE operators are data-intensive and involve a massive volume of ciphertexts, we design the HEU with a 3D stacked memory-based architecture to handle data-intensive operators.We also provide software-level support to facilitate deploying FHE tasks on this extension-based architecture. (2) To capitalize on the hardware parallelism, we propose an adaptive offloading algorithm that intelligently distributes FHE operators between the computing device and the HEU. (3) To optimize the data layout and minimize the inter-tile data communications in the novel 3D stack memory, we propose a dedicated ciphertext mapping mechanism. Experimental results demonstrate that our work achieves substantial acceleration in FHE tasks.

We present Mask, an efficient and tunable algorithm for hiding volume patterns in multi-maps. Volume-pattern leakage, referring to the observable size of data returned by a query, enables adversaries to infer sensitive dataset information, posing severe privacy risks in multi-map scenarios. Designed to address this issue, Mask focuses on balancing storage/query overhead and privacy (a key trade-off in this field), allowing users to define parameters for random data distribution across buckets to realize fine-grained control over storage and query performance, integrating Bloom filters with a bounded cache for efficient indexing (optimizing performance under skewed query workloads); extended to MaskIO for reduced client-side storage, it obfuscates query indexes and uploads them to the server, achieving constant-bounded client storage overhead, and experiments show Mask outperforms the bucket-based peer Veil with 2–4 × higher performance and a lower stash ratio.

The massive growth of data has brought vigorous vitality to Internet-of-Things (IoT). It has also brought new challenges, such as confidentiality privacy protection and redundant data transmission. Concerning this regard, data aggregation serves as an efficient technique to minimize the transmission frequency among massive objects in smart grid(SG). By aggregating a large amount of the same type of data while satisfying the protection of user privacy. With the advent of the post-quantum era, a good aggregation scheme must provide quantum resistance while ensuring the secure aggregation of ciphertext power data. However, the excessive overhead limits anti-quantum algorithms from being widely used in SG where resource devices are limited. Therefore, it is an important part of the current private data security aggregation technology to find a low cost and lightweight inverse quantum algorithm to achieve user data security aggregation. In this paper, we propose an improved NTRU-based cryptosystem with multidimensional coding, referred to as multidimensional coding NTRU (MC-NTRU). and use the lattice batch signature technique, which improves the efficiency of the scheme while satisfying the anti-quantum attack. Based on these, we design the multidimensional auditable lattice-based privacy-preserving data aggregation scheme(MA-PPDA) for privacy data on resource-limited IoT devices such as smart grids. In addition to this, the scheme achieves fault tolerance of the scheme by adding zeros and random numbers to the user data. The comparative study against existing approaches demonstrates that the proposed scheme not only adheres to critical security aspects including user privacy, data confidentiality, integrity, and authenticity, but also decreases both communication and computational burdens on the system. This makes our scheme particularly apt for IoT environments characterized by constrained device resources.

Federated Learning (FL) enables collaborative model training while preserving data privacy, making it particularly attractive for large-scale Internet of Things (IoT) systems. However, in practical deployments, data collected by distributed clients are often non-independent and identically distributed (Non-IID), which amplifies the vulnerability of FL to poisoning attacks. Among them, Label-Flipping Attacks (LFA) are especially stealthy, as they can induce targeted misclassification without noticeably affecting overall accuracy, posing serious risks to safety-critical IoT applications. In this paper, we propose NeuDFL, a lightweight and robust defense framework against LFA under Non-IID settings. Unlike many existing defenses that primarily rely on gradient analysis over the full model update space or auxiliary clean datasets, NeuDFL exploits lightweight class-wise parameter statistics extracted from the final fully connected layer. By leveraging the cumulative and task-aligned nature of model parameters, NeuDFL enables reliable identification of attacked classes and filters malicious clients via an adaptive statistical threshold, improving robustness to data heterogeneity while incurring low computational overhead. Extensive experiments on multiple datasets demonstrate that NeuDFL offers an effective and efficient defense against label-flipping attacks, providing a robust solution for federated learning in complex real-world environments.

Vertical Federated Learning (VFL) is a distributed machine learning paradigm designed for scenarios with vertically partitioned data features, making it highly compatible with Internet of Things (IoT) ecosystems. While promoting collaborative modeling among IoT devices, VFL also introduces new security risks, particularly backdoor attacks. Existing VFL backdoor attacks typically establish associations between triggers and target labels during the training phase by manipulating intermediate model outputs, making them easily detectable by advanced defense mechanisms. This paper proposes Hijacking-based Backdoor Attack (HBA), which for the first time innovatively achieves backdoor attack by exchanging the forward embeddings during VFL prediction phase, without embedding traditional triggers. HBA leverages intrinsic semantic relationships in the embedding space to hijack the decision-making process of the top model during inference. HBA’s effectiveness depends on the discriminative nature of the features extracted by the bottom model, and since it does not alter the training process, it can evade most defense mechanisms based on training behavior monitoring. Experiments demonstrate that HBA achieves an attack success rate of 99.9% in classification tasks without compromising the original task’s accuracy. Furthermore, existing defense mechanisms struggle to effectively counter HBA without degrading the model’s original task performance.

Promoting data sharing is one of the critical strategies for thriving in the digital age, and enormous demand for location-based services propels data sharing in the Internet of Vehicles (IoV), particularly in the case of integrating mobile crowdsensing (MCS). However, data security and privacy concerns are increasingly posing serious challenges to the development of data sharing. Although a slew of works have been designed to achieve secure data sharing in IoV, they are inadequate for addressing the privacy issues identified in the data sharing model and often suffer from performance limitations. In this work, we propose an efficient and secure data sharing scheme under the MCS-integrated IoV. Specifically, motivated by the distributed point function (DPF), we design a double-output DPF and leverage it to construct a secure updating scheme that protects full privacy while ensuring high efficiency. Then, based on the XOR filter and a series of subtle transformations, we carefully design a secure spatial test protocol to determine whether a point falls within an arbitrary spatial range efficiently. Afterward, we propose a secure retrieving protocol by using the idea of shared shuffling, in which the offline sub-protocol is presented to generate random masks, and the online sub-protocol is designed to quickly retrieve the desired data items. After formally proving the security of our proposed schemes, we experimentally evaluate their efficiency by comparing them with the alternative solutions, and the results indicate that our proposed schemes offer superior performance, particularly in terms of communication overheads.

Federated learning is a distributed learning approach designed to train models across multiple client devices with local data while preserving the privacy of that data. However, traditional federated learning suffers from three major f laws: 1.Vulnerability to Byzantine Attacks. 2.Privacy Leakage Risks. 3.Assumption of Third-Party Server Reliability. Few solutions currently address all three of these flaws at the same time. To address the above issues simultaneously, this paper introduces a multi-server federated learning approach that enhances security and privacy. We first set up a multi-server architecture to address the performance issues and single point of failure in traditional federated learning frameworks. And we set up and update different servers reputation on both the server and participant sides, which work together to resist aggregation attacks that can be implemented by malicious servers. It is also worth noting that we split models to be uploaded into two segments, each of which is uploaded to two different servers in conjunction with the last model of the previous round to defend against privacy attacks and keep model as complete as possible. Experiments on MNIST, IMDB, CIFAR10, and Adult indicate this method maintains accuracy, defends against label-flipping and Gaussian noise, and protects privacy.

Searchable encryption (SE) allows users to perform private queries on encrypted databases. Although SE schemes can protect data privacy, some often pursue high performance while allowing certain leakages, such as search and access patterns. Exploiting such leakage together with other knowledge similar to the user’s database, an attacker can recover queries. State-of-the-art attacks (Nie et al., USENIX’ 24) on single-keyword queries achieve accuracies exceeding 90%. More recently, the community has focused on the more challenging attack of recovering multikeyword queries, with the most advanced attacks (Liu et al., TIFS’ 25) achieving over 80% accuracy. Although these attacks can effectively recover queries, they all rely on a large amount of similar document, requiring the attacker to possess documents equivalent in volume to the database. This naturally raises a question: Can we achieve higher-accuracy attacks using less similar data? Less information makes attacks easier to implement. Motivated by this, we present Mirage, an attack that recovers both single-keyword and multi-keyword queries while requiring only partial similar data. Our core idea is to first identify some special queries and design a series of novel algorithms to recover them. Then, partially reconstruct the database index and recover the remaining queries. Extensive experiments conducted across various real-world datasets demonstrate the effectiveness of our attack. The results show that when the attacker observes 51 time intervals and obtains only 0.5% of similar documents in each interval, Mirage achieves 91.6% and 95.4% accuracy on the Enron and Lucene datasets for single-keyword queries, respectively. For multi-keyword queries, Mirage achieves up to 90.7% and 93.5% recovery accuracy, respectively.

In the context of the Internet of Things (IoT), the large-scale generation and collection of data can greatly improve the quality of service provided, but they also raise significant concerns about privacy breaches. However, existing privacy-preserving data collection solutions based on local differential privacy (LDP) often struggle to balance security and accuracy when handling composite data types. To address this challenge, in this paper, we propose CSKV, a high-precision and privacy-preserving key-value data collection scheme. Specifically, we first design a padding and sampling protocol to improve data utility. Then, we propose two randomized response mechanisms to safely perturb keys and values in a cohesive and segmented manner. After that, by leveraging the sampling protocol and key-value correlation perturbation, we demonstrate that CSKV can provide secondary privacy amplification. Detailed theoretical analysis verifies the security and effectiveness of CSKV. In addition, extensive performance evaluations are conducted on synthetic and real-world datasets, and the results indicate that our proposed scheme outperforms existing schemes in terms of hit rate and estimation variance.

Local differential privacy (LDP) provides strict privacy guarantee in a distributed environment. Recent studies demonstrated that LDP protocols are vulnerable to data poisoning attacks where an attacker can manipulate the perturbed result on the local side and send bogus data to skew the final estimate on the server. Unfortunately, existing attack detections do not create an effective attack indicator and rely on particular characteristics of LDP protocols. As a result, they typically exhibit limited detection performance. In this paper, we use log-likelihood as the attack indicator and propose a chain-style detection to enhance the detection effectiveness, in which the attack impact could propagate along the chain and exhibit clear anomaly signal even under stealthy attack scenarios. The experimental results show that our detection consistently outperforms the existing methods. Using four datasets containing categorical and numerical data separately, our detection achieves an F1 score exceeding 96% in most cases. It even remains above 0.9 under stealthy attack settings, outperforming the state-of-the-art detection by up to 0.25.

With the rapid advancement and widespread ap plication of the graph neural network (GNN), the collaborative graph learning, in which multiple parties collaboratively construct a GNN model using their respective graph data, has attracted increasing attention. However, this paradigm also raises significant privacy concerns, as both nodes and edges may contain sensitive personal information, while existing privacy preserving schemes often come at the cost of degraded model performance or substantial system overhead. Therefore, this paper proposes an efficient and privacy-preserving collaborative, and Hui Li, Member, IEEE, Xiaoyu Kou Social Platform learning framework on vertically partitioned graph data, dubbed Plog. Specifically, we first design a decomposition algorithm to split the sparse adjacency matrix into the summation of multiple independent permutations, which are lightweight, parallelizable, and well-suited for secure multi-party computation. Building on this, a weighted oblivious batch permutation protocol is carefully customized based on correlated randomness to securely and efficiently compute adjacency matrix multiplications, addressing the core efficiency bottleneck in GNN inference and training. The selective security of Plog is formally verified under the ideal-real paradigm. Extensive experimental results on three real world datasets demonstrate that compared to the state-of-the art scheme, Plog can reduce online communication rounds by 46% and achieve a 1.73× speedup in the overall inference and training time.

Currently, Unmanned Aerial Vehicles (UAV) groups can quickly build a multi-hop transmission network, which have been widely utilized in emergency communication scenarios to perform search and rescue, environmental monitoring, personnel positioning, rapid networking, etc. In such emergency rescue situations, strict demands on real-time communication, security, and minimal resource consumption become paramount. Higher requirements for security, bandwidth, and real-time performance necessitate a secure and lightweight data transmission protocol. Additionally, due to the lack of personnel supervision in these scenarios, the probability of malicious nodes increases. Therefore, it is essential to quickly and proximally block malicious nodes’ data to prevent it from affecting subsequent network propagation, and to accurately identify the malicious nodes. To address these issues, in this paper, we propose a traceable, lightweight, and secure data transmission protocol for UAV multi-hop networks in emergency rescue scenarios. The proposed protocol can verify the integrity of data transmitted by a large number of nodes in real time, detect erroneous transmissions, and trace malicious users. Experimental results show that our protocol consistently outperforms the comparison schemes in terms of computational overhead. Moreover, in scenarios involving smaller groups (m=5) and fewer hops (n=4), it exhibits significantly lower communication bandwidth overhead than the reference methods. Security analysis using BAN logic and the formal verification tool Scyther indicates that the proposed scheme meets security requirements. Additionally, comparative analysis results demonstrate that the proposed scheme is highly effective and outperforms other related schemes under the unique constraints of emergency rescue scenarios, where rapid, secure decision-making and data transmission are critical.

With the slowdown of Moore’s Law, conventional monolithic chip architectures face challenges such as excessive die sizes and prohibitive manufacturing costs. Consequently, chiplets have emerged as a pivotal technology in the post-Moore era, attracting significant attention from both academia and industry. Multi-chiplet systems offer compelling advantages over monolithic ones, including enhanced integration density, reduced cost, and shortened time-to-market. However, realizing these benefits necessitates design flows capable of optimizing parameters across logical, physical, and circuit layers, which introduces substantial design complexity. Numerous design automation technologies have been proposed to address these challenges. This article provides a comprehensive overview of related advancements, categorizing chiplet design methodologies into two primary types: (i) top-down flows disintegrating existing hardware designs into chiplets and subsequently reintegrating them into multi-chiplet systems, and (ii) bottom-up flows combining existing chiplets into multi-chiplet systems based on user applications. This article begins by introducing foundational concepts, technical characteristics, and evaluation models relevant to multi-chiplet systems. We then systematically summarize the problem formulations, design spaces, and optimization techniques associated with top-down and bottom-up design flows. Finally, we discuss key challenges and potential future research directions in chiplet design automation, aimed at further harnessing the potential of chiplet-based integration.

Online social networks (OSN) such as Twitter, Facebook, etc. have an overall user base of more than 5 billion as of today. Traditional centralized OSN users’ data and content are stored in centralized servers, which has the risk of data leakage and privacy violation. Distributed OSN (DOSN) address the single-point-of-failure and user data privacy concerns faced by centralized OSN by enabling the operation of network infrastructures and services without centralized ownership or control. However, DOSN face privacy protection issues. The group key agreement (GKA) is an important method to construct secure channels to protect the secure communication of network group members. Asymmetric GKA methods allow external members to securely communicate with group members without having to join the group. However, it is worth noting that existing AGKA schemes rely on bilinear pairing, resulting in a high computational overhead. Meanwhile, considering scenarios where external users join, or group members leave, we design a dynamic asymmetric group key agreement (DAGKAwP) scheme based on Schnorr batch multi-signature that does not depend on bilinear pairing. During the key generation phase, the members generate self-authenticating public-private key pairs to resist malicious public key attacks. For group key agreement, new hash functions are embedded in Schnorr signatures to generate aggregated public keys, and this scheme supports external member addition and internal member exit. In group message encryption, sender anonymity and message non-repudiation are realized. The security comparison with related DAGKA schemes reveals that the DAGKAwP scheme offers more comprehensive security. Performance evaluations suggest that this scheme offers computational efficiency and lower communication overhead than related Dynamic AGKA schemes.

Fully Homomorphic Encryption (FHE) allows computations on encrypted data. However, FHE faces significant performance overhead due to the time-consuming homomorphic multiplications and bootstrapping operations, which limits its practical applications. Meanwhile, many FHE optimization schemes have been proposed based on a Trusted Execution Environment (TEE), but they heavily rely on the trustworthiness of the TEE. In outsourcing scenarios, the TEE owner might be a third-party service provider, posing a risk of data leakage for users. To address these issues, we propose an efficient BGV scheme with semi-honest TEE assistance. We consider the TEE to be honest-but-curious, meaning the TEE owner might eavesdrop on the user's data during computation. Based on this assumption, we design a new BGV homomorphic multiplication protocol that operates in a smaller ciphertext space without compromising the original security level and substantially improves computational efficiency. With the assistance of a semi-honest TEE, our multiplication protocol eliminates the need for the switch key, reducing initialization time and supporting an unlimited number of homomorphic multiplication operations without bootstrapping. Furthermore, we provide a security proof using a simulationbased real/ideal world model. Extensive experiments show that our scheme outperforms mainstream libraries such as SEAL, OpenFHE, and Helib.

In the era of cloud computing and AI, data owners outsource ubiquitous vectors to the cloud, which furnish approx-imate k-nearest neighbors (k-ANNS) services to users. To protect data privacy against the untrusted server, privacy-preserving k-ANNS (PP-ANNS) on vectors has been a fundamental and urgent problem. However, existing PP-ANNS solutions fall short of meeting the requirements of data privacy, efficiency, accuracy, and minimal user involvement concurrently. To tackle this challenge, we introduce a novel solution that primarily executes PP-ANNS on a single cloud server to avoid the heavy communication overhead between the cloud and the user. To ensure data privacy, we introduce a novel encryption method named distance comparison encryption, facilitating secure, efficient, and exact distance comparisons. To optimize the trade-off between data privacy and search performance, we design a privacy-preserving index that combines the state-of-the-art k-ANNS method with an approximate distance computation method. Then, we devise a search method using a filter-and - refine strategy based on the index. Moreover, we provide the security analysis of our solution and conduct extensive experiments to demonstrate its superiority over existing solutions. Based on our experimental results, our method accelerates PP-ANNS by up to 3 orders of magnitude compared to state-of-the-art methods, while not compromising the accuracy.

In the landscape of the Internet of Things (IoT), the rapid evolution and diverse obfuscation tactics of malware render it challenging to detect and identify effectively, posing significant threats to network security. Signature or heuristic methods rely on fixed feature recognition, making it challenging to handle new variants. Recent research has proposed deep learning techniques that utilize static analysis of bytes and images or dynamic analysis of APIs. However, these methods are effective only on samples from the same platform or lead to a dimensional explosion due to excessive irrelevant obfuscation, rendering them inadequate for managing complex cross-platform malware. In this work, we propose a novel lightweight cross-platform malware classification system called MalAE. This system employs a global-local particle swarm optimization algorithm to mine frequent features, adaptively identifying distinct family characteristics and efficiently recognizing variants. An ensemble of autoencoders integrates comprehensive file features and cross-platform basic block features from various perspectives and feature spaces, compressing high-dimensional data into a low-dimensional latent space. This approach preserves essential information, captures nonlinear complex relationships, and facilitates the rapid classification of intricate cross-platform samples. Evaluations conducted on two different datasets demonstrate that MalAE reduces the original feature dimensions by approximately 70% while also enhancing accuracy. Compared to state-of-the-art methods, MalAE achieves superior results, attaining an accuracy of 97.72%.

Introducing slicing into integrated satellite-terrestrial networks enables the flexible deployment of network resources and being adaptable for more new applications. However, the heterogeneity of integrated satellite-terrestrial network poses challenges to network resource access control. To ensure users can securely and efficiently access service across multiple management domains, we propose a network slice access authentication and service authorization scheme based on a sharding permissioned blockchain. Slice tenants and wireless network operators with management control act as consortium blockchain nodes, which are divided into shards, and the blockchain is maintained in parallel by multiple shards. First, an efficient public ledger is constructed to establish decentralized trust and manage user identity and service authorization information. Second, utilizing the trapdoor collision resistance of the chameleon hash, users can fully self-select their secret key and generate authentication credentials to register on the blockchain without key escrow problem. When users move into a new network domain, the mutual authentication between users and the visited network can be quickly completed. The session key is negotiated based on the Diffie-Hellman ephemeral protocol with perfect forward secrecy. Then, the editable transaction blocks, storing network slice authorization information and slice templates, are linked using chameleon hashes. This allows the access permissions of slice resources to be dynamically adjusted and easily queried by the service-providing wireless network operators. Performance evaluation and security simulations demonstrate the correctness of the scheme, showing that it can achieve secure access to integrated satellite-terrestrial network slice services with low computational and communication overhead.

In the field of Internet of Things (IoT), data distillation has been thought of as a key method to condense the original real dataset into a tiny synthetic dataset with less training burden while maintaining as much data utility as possible for training deep learning models. However, the data synthesis process may remember some sensitive information about the original dataset, which may raise privacy concerns for data owners. To address this problem, we present a novel differential privacy (DP)-based data distillation algorithm. Specifically, in the data distillation phase, we first randomly pick a training model from the model pool in each epoch, and then build a fine-grained distribution matching to generate informative data for improving the task-oriented model performance. In the privacy preservation phase, we selectively perturb input features that are more important for model training based on the sparse vector technique to protect the sensitive information contained in the original dataset and reduce privacy costs. Extensive experiments across several real-world datasets demonstrate that our algorithm can achieve higher data utility and model accuracy than existing solutions.

Voting protocols are fundamental in modern society. With the ongoing evolution of communication technology and the Internet of Things, the importance of electronic voting protocols is anticipated to experience a significant rise in the advancement of smart cities. Leveraging the blockchain’s tamper-resistant and publicly verifiable properties, along with the concept of enabling all participants to tally election results, blockchain-based self-tallying voting protocols effectively address the shortcomings of centralized traditional electronic voting. However, existing voting protocols still face the dual challenge of security and efficiency. The primary issues include the difficulty in ensuring voter anonymity and election fairness, as well as the insufficient system robustness and low tallying efficiency resulting from security design. To tackle these concerns, we propose a novel approach to constructing efficient and flexible voting protocols that concurrently ensure fairness and anonymity. Specifically, we encapsulate the decryption private keys corresponding to the public keys of encrypted ballots in time capsules to safeguard voting fairness. Additionally, based on our proposed approach, we construct an efficient and flexible score voting protocol for smart cities. The protocol employs traceable ring signature to protect the voter anonymity and public traceability, utilizes a dual-key additive homomorphic ElGamal encryption to encapsulate ballots. We also improve signature-based set membership proofs to verify the validity of ballots. Furthermore, through security analysis and performance evaluation, we demonstrate that our proposed protocol meets all security objectives with reasonable costs.

Ciphertext retrieval technology has been widely explored with the increasing popularity of cloud computing. Proxy re-encryption with Keyword search (PREKS) can support multi-user retrieval without increasing data owner's overhead, but users can continue searching once they have obtained search rights. It is difficult for a data owner to revoke a user's access rights because the data is stored in the cloud. In general, under the premise of forward and backward security, the user's search permission can be revoked by updating the ciphertext. Nevertheless, this approach may expose user or data privacy to cloud servers. In this paper, we utilize the Chinese Remainder Theorem to generate DO-Authorization and DU-Authentication factors, and realizes the authorization and revocation of a specific single user by adding or deleting authorization items. In addition, we use a designated tester algorithm in the ciphertext retrieval process to improve system security. Subsequent security analysis proves that the proposed scheme can resist the Chosen Keyword Attack and Keyword Guessing Attack. Simulation results indicate that the proposed scheme has high efficiency, especially in the user revocation phase

Electronic Health Record (EHR) sharing can improve the accuracy of medical diagnosis and promote the development of the public medical field. However, plaintext medical records expose patient privacy, and it is challenging to share medical records between different hospitals, making it difficult for doctors to securely retrieve medical records and massively analyze the same diseases from different patients. In this paper, we propose a flexible ciphertext retrieval scheme in electronic health sharing system that supports multi-patient and multi-keyword medical records retrieval. The proxy free ciphertext retrieval technology based on cloud servers enables doctors to retrieve multiple patients' medical records simultaneously. On this basis, we utilize the time encoding algorithm to ensure that the trapdoor associated with the old time cannot retrieve the ciphertext index bound with the latest time, so as to realize the lightweight forward security. Besides, the encoding time signed by the deniable signature can avoid forgery by malicious users or adversaries. In addition, the scheme supports evil user traceable and revocable. The security analysis indicates that the scheme can satisfy the keyword privacy, the forward security of updated ciphertexts, and the deniability of time encoding signatures. Experimental evaluation illustrates that our scheme is lightweight and efficient

5G Proximity-based Service (ProSe) UE-to-Network (U2N) Relay can help a remote 5G User Equipment (UE) out of coverage connect with the network. The 3GPP committee has provided the standard Authentication and Key Agreement (AKA) protocols to achieve secure access for a Remote UE and establish a secure link between a Remote UE and a U2N Relay. However, the security of these AKA protocols is a remaining issue. At first, we present two detailed 5G ProSe AKA protocols over Control Plane (CP) and User Plane (UP) for U2N Relay communication referring to multiple related standards, then transform the security requirements for 5G ProSe U2N Relay communication as formal security properties, and provide two formal faithful security models for the 5G ProSe AKA protocols. We adopt the state-of-the-art formal verification tool Tamarin to achieve automated security analysis on two models through new proof strategies, and then find some significant and unexpected flaws. Finally, we propose corresponding measures that have least impact on standards based on the analysis on the attacks. Given that the version of Release 17 (R17) of the 3GPP standard has just been frozen, our work can provide a reference for the subsequent evolution of the protocols in 5G ProSe.

In recent years, multi-dimensional range query (MRQ) over encrypted data has been increasingly applied in various scenarios, making it one of the mainstream services for secure large-scale data storage and sharing in cloud computing. However, although existing privacy-preserving MRQ schemes can ensure query and data privacy, they still fail to fully protect single-dimensional privacy and path pattern. Moreover, most schemes lack mechanisms to verify the completeness and correctness of query results, making it impossible to guarantee their validity. To address these issues, this paper proposes a secure and efficient MRQ scheme with result verification for multiple users (MU-MRQ). First, we design a secure and efficient multi-dimensional data index based on the G-tree, incorporating a timestamp mechanism to verify the correctness and completeness of query results. Additionally, we propose two novel intersection predicate encryption protocols to achieve efficient retrieval while preserving single-dimensional privacy and path pattern. Rigorous security analysis demonstrates that our MU-MRQ scheme achieves security under the known background model. Comprehensive experiments on real-world datasets validate the efficiency of the MU-MRQ scheme.

Model poisoning attacks greatly jeopardize the application of federated learning (FL). The effectiveness of existing defenses is susceptible to the latest model poisoning attacks, leading to a decrease in prediction accuracy. Besides, these defenses are intractable to distinguish benign outliers from malicious gradients, which further compromises the model generalization. In this work, we propose a novel proactive defense named${\sf RECESS}$against model poisoning attacks. Different from the passive analysis in previous defenses,${\sf RECESS}$proactively queries each participating client with a delicately constructed aggregation gradient, accompanied by the detection of malicious clients according to their responses with higher accuracy. Furthermore, RECESS uses a new trust scoring mechanism to robustly aggregate gradients. Unlike previous methods that score each iteration, RECESS considers clients’ performance correlation across multiple iterations to estimate the trust score, substantially increasing fault tolerance. Finally, we extensively evaluate${\sf RECESS}$on typical model architectures and four datasets under various settings. We also evaluated the defensive effectiveness against other types of poisoning attacks, the sensitivity of hyperparameters, and adaptive adversarial attacks. Experimental results show the superiority of${\sf RECESS}$in terms of reducing accuracy loss caused by the latest model poisoning attacks over five classic and two state-of-the-art defenses.

Over the years, various mobile communication technologies have been developed and operated simultaneously, which made the mobile communication networks evolved from single-mode access to complex heterogeneous integration. The current 5 G has already accommodated diverse terminals through multiple access paths, but the upcoming 6 G ambitiously aims to achieve ubiquitous connectivity through space-air-ground-sea integrated networks. However, traditional authentication and management protocols, such as EPS-AKA and 5G-AKA, are designed for specific networks and lack the flexibility to adapt to the diverse and dynamic requirements of 6G. This limitation will inevitably result in complex management, enormous overhead, and unmanageable security risks. In this paper, we present an atomic functional module-based 6G authentication protocol combination framework (AotmAuth) to decompose the existing authentication protocols into reusable basic modules, and by combining these modules, flexible authentication protocols can be constructed to meet specific security and performance requirements. The proposed approach can significantly improve the robustness, extensibility and dependability of protocols cobmination, by simplify protocol design, enhance adaptability across diverse scenarios and facilitate quick improvements by replacing or adjusting specific modules. To validate the effectiveness of the proposed solution, we design and develop a 6G heterogeneous access security testbed. The experimental results show that the proposed framework can achieve higher authentication efficiency while ensuring security compared to traditional authentication methods.

The Execute-Order-Validate blockchain enhances performance by allowing parallel transaction execution, yet it also introduces transaction conflicts that can cause state inconsistencies in the ledger. Previous research has focused on resolving conflicts under the assumption of the “good” intent of the senders. In this paper, we explore an unstudied scenario where a malicious user can intentionally generate transaction collisions to disrupt the service request of a target user to the underlying decentralized application (DApp). We call it manipulated transaction collision (MTC) attack. We overcome the challenges of identifying the conditions and best strategies to launch this targeted attack under various network settings. Our experiment results show that the MTC attack can effectively cause the victim to be continuously rejected by the blockchain, i.e., over 90% success rate in all tested cases on the Hyperledger Fabric blockchain. To combat this new threat, we first propose a machine-learning-assisted detection method that helps identify the adversarial behavior within massive background traffic. To further enhance blockchain resilience, we propose a more precise transaction conflicts definition and present a novel mitigation method, which not only prevents the attack but also significantly reduces the probability of natural conflicts by up to 75% in the tested DApp compared to state-of-the-art optimization methods.

Weighted set sampling has been proven essential for generating discrete numbers based on their weights and found broad applications in recommendation systems. The extension of this method, known as weighted range set sampling (WRSS), specifies a query range and applies weighted set sampling to the data within that range. With the proliferation of cloud computing, outsourcing encrypted data and data processing tasks to cloud servers has become a common practice to overcome data storage and processing challenges while protecting data privacy. Existing studies have proposed many privacy-preserving solutions for various customized query and data processing tasks, none have specifically addressed privacy-preserving WRSS. In response to this gap, our paper introduces an efficient and privacy-preserving WRSS scheme. We begin by leveraging the three-party secret sharing (TPSS) scheme as a foundation to design an enhanced three-party secret sharing (eTPSS) scheme with superior storage and computational efficiency. Building upon the eTPSS scheme, we introduce a series of private algorithms to safeguard WRSS privacy. Our scheme integrates the use of a binary search tree and the alias method for WRSS, ensuring privacy through eTPSS-based private algorithms. A thorough security analysis under the simulation-based real/ideal worlds model showcases the effectiveness of our proposed scheme. The proposed scheme's efficiency has been substantiated through extensive experiments, demonstrating that our scheme marks a significant advancement in addressing the challenges posed by privacy-preserving WRSS.

In order to ensure data security and improve data usability, searchable encryption has been widely used in cloud computing systems. However, the evil single users with search privileges bring heavy privacy threats to the system. Threshold searchable encryption provides a collaborative search service for group users; a single user cannot search for ciphertext. However, the threshold searchable encryption based on the Shamir secret sharing mechanism cannot achieve flexible user dynamic since the Lagrange interpolation polynomial for recovering the secret value changes with the group user add or delete, resulting in the ciphertext or trapdoor containing Lagrange interpolation formula needs to be recreated. In this paper, the conjunctive keyword search with dynamic group-user scheme (CKSDGU) is proposed to realize group-user flexible addition and deletion. The proposed CKSDGU scheme can match successfully without the data owner resetting ciphertext and the original data user generating trapdoors. In addition, multi-keyword conjunctive retrieval is implemented in the CKSDGU scheme, and group users can search the target ciphertexts that contain all users’ query keyword sets. The security analysis illustrates that the CKSDGU scheme can resist chosen keyword attacks and keyword guessing attacks. The performance analysis presents that our scheme has considerable overhead and efficient computational cost in the user dynamic stage.

Over-Threshold Multi-Party Private Set Intersection (OT-MPSI) is a variant of MPSI that aims to return items that appear in at leastTof participants’ sets without revealing any other information. OT-MPSI is applicable to many practical scenarios and offers an advantage over MPSI when identifying items held by most but not all participants. The existing work processes binary vector representations of sets in a bit-wise manner and utilizes Secure Computation Protocols to achieve over-threshold functionality. This results in low computational efficiency, with the number of communication rounds scaling linearly with the number of participants. We propose an efficient OT-MPSI protocol (EoTMP) by utilizing ring learning with errors based multi-party homomorphic encryption. By introducing a new over-threshold functionality and leveraging additional optimization techniques, our EoTMP requirs only three communication rounds and offers faster computation than the state-of-the-art. In addition, our scheme supports thet-Nthreshold access-structure for participant collaboration. Specially, with 45 participants and a threshold of 40, EoTMP processes sets of size 256 in 0.6 seconds, achieving a reduction in computational overhead by three orders of magnitude compared to prior work.

Local differential privacy (LDP) provides lightweight and provable privacy protection and has wide applications in private data collection. Key-value data, as a popular NoSQL structure, requires simultaneous frequency and mean estimations of each key, which poses a challenge to traditional LDP-based collection methods. Despite many schemes proposed for the privacy protection of key-value data, they inadequately solve the condensed perturbation for keys and the advanced combination of privacy budgets, leading to suboptimal estimation accuracy. To address this issue, we propose an efficient key-value collection scheme (COKV) with tight privacy budget composition. In our scheme, we first design a padding and sampling protocol for key-value data to avoid privacy budget splitting. Second, to enhance the utility of key perturbation, we design a key perturbation primitive and optimize the perturbation range to improve computational efficiency. After that, we propose a key-value association perturbation algorithm whose value perturbation strategy guarantees the output expectation equals the original value. Finally, we demonstrate that through a tight privacy budget composition, COKV can provide higher data utility under the same privacy level. Theoretical analysis shows that COKV possesses lower frequency and mean estimations variance. Extensive experiments on both synthetic and real-world datasets also indicate that COKV outperforms the current state-of-the-art methods for secure key-value data collection.

The rapid development of big data and Internet of Things has promoted the formation of data silos, and cloud computing has facilitated the outsourcing of vertically distributed data to cloud servers. In outsourced query scenarios, building query indexes is crucial for balancing data utility and data privacy protection. The hypercube tree is a widely used index for multi-dimensional data, supporting various query types. Although secure hypercube tree-based queries have been extensively studied in existing works, they are not applicable for building a hypercube tree over vertically distributed ciphertext data. To address this issue, we propose the first efficient and secure hypercube tree building scheme for vertically distributed data, named SCTBuild. We first design a flexible three-party secret sharing (fTPSS) scheme, allowing data owners to flexibly configure secret sharing forms based on real-world computational, communication, and storage constraints. Then, we design a communication-efficient data outsourcing algorithm, a secure data permutation algorithm, and a secure data comparison algorithm based on the fTPSS scheme. After that, we propose our SCTBuild scheme based on the aforementioned algorithms, in which data owners first perform pre-computation on their data to improve tree-building efficiency. We prove that our fTPSS scheme, private algorithms, and the SCTBuild scheme are semantically secure in the simulation-based real and ideal worlds security model; and conduct experiments to validate their high efficiency.

With urban population density on the rise, emergency incidents are increasing in both frequency and complexity, placing growing pressure on existing rescue systems. Meanwhile, issues such as slow response times, inadequate coordination mechanisms, and inefficient information exchange further exacerbate the challenges faced by these systems. The integration of Vehicle-to-Everything (V2X) communication and 5G technology offers unprecedented capabilities, such as ultra-low latency and high data throughput, which are critical for real-time coordination and decision-making in emergency rescue scenarios. To establish secure and efficient vehicle communication in 5G and V2X-enabled emergency rescue scenarios, we propose an efficient vehicle secure scheduling and access authentication scheme based on certificateless cryptography and multireceiver signcryption. In this scheme, the command and control center can securely dispatch rescue fleets based on disaster conditions. By enabling mutual authentication and key agreement between rescue vehicles and roadside units, the scheme ensures the reliable and swift exchange of rescue information and instructions. In addition, to address unexpected situations such as traffic congestion, we design a route-switching mechanism. Furthermore, in order to mitigate potential malicious behavior, a vehicle legitimacy revocation mechanism is implemented to ensure the normal operation of the system. The security of the scheme is verified through formal analysis and informal analysis. Performance analysis demonstrates that the scheme offers significant advantages over existing ones in terms of signaling overhead, communication overhead, computational overhead, and energy efficiency.

The CAN bus is the most widely used bus for intra-vehicle communication due to its high transmission stability, excellent real-time communication capability, and relatively low cost. As the number of ECUs grows, the CAN bus load increases and thus raises the possibility of data transmission delays and errors. Message compression based on the differential algorithm has been proposed to reduce the CAN bus load. However, current works do not consider the security problems of the CAN bus. Attackers can manage to acquire the original messages before compression and disturb the message statistics to decrease compression rate by injecting malicious frames. In this paper, we propose a secure compression mechanism for the intra-vehicle network, including an improved compression algorithm, a stream key distribution scheme, and a hierarchical encryption scheme. Formal verification results show that the proposed scheme can achieve mutual authentication, message confidentiality and integrity, resist replay attacks, and support secure compression. Evaluations using real vehicle data on 16 MHz boards show the average communication overhead can be reduced by 46.38% compared to the original messages. Performance analysis results show our scheme can reduce computational overhead on compression by 31.82% and 19.43% on decompression compared to related schemes.

Federated Learning (FL) is a promising privacy-preserving machine learning paradigm that allows data owners to collaboratively train models while keeping their data localized. Despite its potential, FL faces challenges related to the trustworthiness of both clients and servers, particularly against curious or malicious adversaries. In this paper, we introduce a novel framework namedFederatedLearning with Low-DimensionalUpdateRepresentation andProximity-Based defense (FLURP), designed to address privacy preservation and resistance to Byzantine attacks in distributed learning environments. FLURP employs$\mathsf {LinfSample}$method, enabling clients to compute the$l_{\infty }$norm across sliding windows of updates, resulting in a Low-Dimensional Update Representation (LUR). Calculating the shared distance matrix among LURs, rather than updates, significantly reduces the overhead of Secure Multi-Party Computation (SMPC) by three orders of magnitude while effectively distinguishing between benign and poisoned updates. Additionally, FLURP integrates a privacy-preserving proximity-based defense mechanism utilizing optimized SMPC protocols to minimize communication rounds. Our experiments demonstrate FLURP's effectiveness in countering Byzantine adversaries with low communication and runtime overhead. FLURP offers a scalable framework for secure and reliable FL in distributed environments, facilitating its application in scenarios requiring robust data management and security.

Ultra-High-Speed Terminals (UHSTs) can transport multiple Load Equipment (LEs) to precise locations, such as in a space station resupply mission scenario. In these scenarios, UHSTs need to access ground networks via satellite networks. However, since the connections between UHSTs and ground networks are established through insecure air interface channels, they are susceptible to attacks such as eavesdropping, impersonation, and other. Furthermore, owing to the high-speed mobility of UHSTs, they may not be able to connect successfully to the ground network through a single access point, which is possible for regular terminals. Additionally, UHST may also need to communicate with multiple LEs, which is also connected via insecure air interface channels. Therefore, this paper proposes a secure access and intra-group authentication scheme for UHSTs in satellite network scenarios. In the proposed scheme, based on pre-shared keys and trajectory prediction mechanisms, the UHST can successfully access the ground network through multiple access points and complete key establishment with the access points along its trajectory in advance. Using Shamir’s (t, n) Secret Sharing mechanism, UHST and multiple LEs can share a group key, ensuring secure intra-group data communication. Additionally, when one LE detaches from the UHST, the UHST can authorize the LE to access the ground network. Security and efficiency analysis shows that the proposed scheme achieves comprehensive security features with low overhead.

The advent of MiniApps, operating within larger SuperApps, has revolutionized user experiences by offering a wide range of services without the need for individual app downloads. However, this convenience has raised significant privacy concerns, as these MiniApps often require access to sensitive data, potentially leading to privacy violations. Despite existing privacy regulations and platform guidelines, there is a lack of effective mechanisms to safeguard user privacy fully. To address this critical gap, we introduce MiniScope , a novel two-phase hybrid analysis approach, specifically designed for the MiniApp environment. This approach overcomes the limitations of existing static analysis techniques by incorporating UI transition states analysis, cross-package callback control flow resolution, and automated iterative UI exploration. This allows for a comprehensive understanding of MiniApps’ privacy practices, addressing the unique challenges of sub-package loading and event-driven callbacks. Our empirical evaluation of over 120K MiniApps using MiniScope demonstrates its effectiveness in identifying privacy inconsistencies. The results reveal significant issues, with 5.7% of MiniApps over-collecting private data and 33.4% overclaiming data collection. We have responsibly disclosed our findings to 2,282 developers, receiving 44 acknowledgments. These findings emphasize the urgent need for more precise privacy monitoring systems and highlight the responsibility of SuperApp operators to enforce stricter privacy measures.

Modern vehicles are equipped with ECU nodes and intra-vehicle buses. Among these, the CAN bus stands out as the most widely utilized intra-vehicle bus due to its affordability and straightforward deployment. However, the CAN bus suffers from significant security vulnerabilities, such as the absence of access control, identity authentication, message encryption, and authentication. In this paper, we propose a lightweight message-based key distribution scheme aimed at addressing these vulnerabilities. Our scheme facilitates mutual authentication during key distribution and assigns a unique key to each class of message. Formal verification using the Scyther tool demonstrates that our protocol achieves mutual authentication and effectively mitigates several protocol attacks, including replay, tampering, and manin-the-middle attacks. We evaluate our scheme using Arduino UNO boards. Performance analysis indicates that our scheme exhibits superior security capabilities compared to other related schemes and outperforms them in terms of communication and computation overheads.

The right to deletion mandates that data controllers receiving deletion requests not only erase the specified data but also notify other controllers to do the same. Designing a secure and privacy-preserving protocol for distributing deletion notifications across involved data controllers, a topic not previously addressed in the literature, presents significant challenges. In this paper, we introduce CAREFUL, a secure and privacy-preserving deletion notification distribution protocol—the first to address these challenges. It operates within a centralized architecture consisting of regulatory and service planes. The fundamental principle of CAREFUL is that the regulatory plane creates a cryptographic access control structure, ensuring that data controllers in the service plane only identify the next-hop nodes for notification. With CAREFUL, the circulation history of data pending for deletion, which is a special user’s privacy, can be preserved. Moreover, CAREFUL protects the deletion notification distribution from various malicious attacks over untrusted underlay networks. Experimental results validate CAREFUL’s practicality and its efficiency in terms of resource overhead.

Visible and infrared image fusion (VIF), combining thermal information with textural details for more informative output, has attracted widespread research interest. However, current studies in VIF primarily focus on enhancing the image quality of fusion results, while the robustness against adversarial attacks remains largely unexplored. The VIF system might be deceived to produce inferior output that affects the decision of subsequent high-level tasks, leading to potential security accidents. This work aims to bridge this gap. We focus on degrading the fusion quality by introducing an adversarial attack, which injects imperceptible perturbation into input images to deteriorate the fusion result. To this end, we design an adaptive target generation method to establish a benchmark that indicates the ideal attack effect, which is used to guide the generation of perturbations. Extensive experiments demonstrate that our attack effectively impairs the performance of VIF models, while concurrently exerting a notable impact on subsequent tasks.

In recent years, cloud computing services have grown rapidly, with people outsourcing huge amounts of private data to cloud servers. Searchable encryption(SE) facilitates people’s use of data while protecting data privacy. To balance the efficiency, current SE schemes still leak information such as access, search and volume patterns to cloud servers, and most dynamic SE schemes leak forward and backward privacy, and these leaks create serious security threats. In this paper, we propose a dynamic SE scheme DMASP with suppressed leakage, which protects access pattern, search pattern and volume pattern simultaneously. We utilize the differential privacy mechanism to obfuscate databases, and introduce the CKKS algorithm to protect access and volume patterns during queries. Meanwhile, we design a secure structure to index databases efficiently, and protect forward and backward privacy during updates. We rigorously analyse the security of DMASP. Furthermore, comprehensive experimental evaluation show that DMASP can reduce the accuracy of attacks against patterns leakage in real-world databases.

Machine learning-based trajectory-user linking (TUL) task, which explores human mobility patterns and identifies known users from anonymous trajectories, is widely applied in location-based personalized recommendation systems. However, TUL models implicitly and inadvertently retain users historical trajectories in training data, which could be revealed under malicious inference and analysis. To address this issue, we propose UNIRE, a knowledge transfer-style TUL model aggregation framework to protect sensitive training data. Specifically, teacher TUL models are trained with private users trajectories and then utilized to collaboratively annotate a sensitive-trajectories-isolated dataset. Leveraging this teacher-labeled dataset, student TUL model can be trained and released as the secure aggregation of teacher models, preventing attackers from arbitrarily accessing or exposing the private trajectories. Meanwhile, differential privacy is applied for safeguarding the knowledge transfer process, ensuring that aggregation information from individual teacher models is indistinguishable. Furthermore, we introduce trajectory Equalization and Pseudo Classification as data alignment and accuracy optimization mechanisms for training and aggregating, respectively. Finally, security analysis and extensive experiments indicate that UNIRE framework achieves effective sensitive training data preservation and performance-improved TUL models aggregation, compared to state-of-the-art baselines.

XGBoost, a widely-used machine learning algorithm, has been applied across various fields. To develop a high-quality model, federated learning is employed, allowing participants to keep their data localized while sharing gradients for global model training. However, recent studies have shown that gradients can leak sensitive information, and even when protected, they might still be vulnerable to certain attacks. While several privacy-preserving and verifiable schemes have been proposed to address this issue, they still face limitations in terms of computational and communication costs in certain scenarios. To solve the problem, we propose a privacy-preserving and verifiable XGBoost training scheme for horizontal federated learning. First, we propose a masking with one-time padding protocol (MOTP) to securely aggregate gradients, supporting both online and offline modes for generating random masks. Both approaches reduce the communication costs and improve the training efficiency. Next, we present a linear aggregation verification protocol (LAVP) to ensure the gradient integrity, which avoids complex computations, thereby improving verification efficiency. Building on MOTP and LAVP, we propose a privacy-preserving and verifiable internal node construction algorithm to train a XGBoost model for horizontal data. Detailed security analysis and extensive experiments demonstrate that our scheme can achieve the privacy-preserving, verifiable, lossless, and efficient XGBoost training for horizontally partitioned data.

To overcome the challenges of urbanization and population growth, smart cities use cutting-edge technologies, such as IoT and AI to offer improved public services. Although these advancements have brought convenience, they have raised security and privacy concerns. Blockchain technology has the potential to address these concerns, but existing blockchain frameworks have issues of scalability and efficiency that hinder their ability to meet the smart city demands. In this article, we propose a novel blockchain framework for smart cities, named SC-Chain. Specifically, SC-Chain incorporates a decentralized access mechanism that leverages threshold signatures and BFT-like consensus for efficient node registration and authentication in decentralized systems. Furthermore, we propose a consensus mechanism that utilizes the verifiable random function (VRF) to achieve efficient miner node election, ensuring efficiency, fairness, and security in large-scale smart city systems. We demonstrate the effectiveness and feasibility of the SC-Chain through theoretical analysis and simulations, showcasing its potential to enable the development of secure and efficient smart city infrastructure.

The attribute-based encryption (ABE) scheme, which can set specific conditions to control user access to data, has been widely studied and applied to cloud storage services. Considering file hierarchy in practical scenarios, the ABE scheme can set a hierarchical access control policy so multiple files can be associated with one access structure to reduce users’ computing overhead and save the cloud server’s storage space. However, the existing systems have the risk of user collusion due to the hierarchical access control structure parameters. This paper proposes a secure file hierarchy ABE scheme supporting user collusion resistance (CR-FH-CPABE) in cloud computing. We add a data noise vector without changing the hierarchical access control structure to prevent user ultra vires. Technically, we break the relationships that colluding users could exploit, prevent malicious users from colluding with their computing results, and extract meaningful information from the ciphertext. In addition, we provide an improved CR-FH-CPABE scheme with outsourced decryption, which helps resource-limited devices obtain computing services. Finally, we demonstrate our scheme is CPA secure and show outstanding performance through simulation results.

As a new machine learning (ML) paradigm, federated learning (FL) empowers different participants to jointly train a more effective model than traditional ML. Unlike horizontal FL (HFL), which expands the sample space by aggregating local models, vertical FL (VFL) is suitable for scenarios where the sample ID between participants is the same but differs in sample characteristics. For a long time, VFL has been considered safe due to no data exchange and heterogeneity between parties. However, the recently proposed label inference attacks pose a significant security threat to VFL. Specifically, by adding randomly initialized layers to the top of local models, the passive label inference attack can infer tens of thousands of local participants’ private data with only 40 auxiliary labels. Since the attack is entirely local, using privacy protection technologies such as differential privacy cannot effectively defend against these attacks. Therefore, we propose a new privacy protection scheme called FL similar gradients (FLSGs) to defend against this attack. Unlike differential privacy, the FLSG scheme randomly generates gradients of a Gaussian distribution similar in dimension to the original gradients and calculates their cosine distance. If the distance is less than a certain threshold, the gradients are used instead of the original gradients to pass to the local participants. We conducted extensive evaluations on six real-world data sets, and the results show that FLSG provides a better defensive effect at lower computational overhead than other known methods when defending the passive label inference attack.

The fine grain of ciphertext-policy attribute-based encryption (CP-ABE) offers advantages through the amalgamation of key and user attributes; however, it also brings the issue of key misuse. To circumvent the tracking mechanisms of the white-box algorithm, malicious users manipulate the decryption key and encryption algorithm, encapsulating them to create a black-box decryption device. This necessitates black-box traceability for supervision purposes. In this article, we employ$n$-bit encrypted binary vectors to depict the user’s identity and subsequently convert it into partial decryption privileges. When encrypting plaintext, data owners can utilize a “vague specification” to define the identity vector of qualified decryptors. Furthermore, based on the vague specification mechanism, we have devised a pioneering active black-box tracing algorithm. Integrating this algorithm with CP-ABE, we propose the black-box traceable CP-ABE (EIV-BT-ABE) scheme. Our EIV-BT-ABE scheme attains strong traceability with low time complexity, effectively reducing decryption and encryption time costs. The experiment substantiated the efficiency of our scheme while demonstrating its adherence to IND-CPA security.

The smart grid, which is an important component of smart cities, is developing rapidly nowadays, while the confidentiality and integrity of consumption data of customers become significant security issues. Although existing privacy-preserving aggregation schemes reduce the communication overhead and protect the privacy of users’ multidimensional power data, most of them are vulnerable and possess no quantum-resistant properties. In this article, we combine the Chinese remainder theorem (CRT) and the bit decomposition method to provide multibit homomorphic properties for the quantum-resistant algorithm number theory research unit (NTRU), and propose the partial-homomorphic NTRU (PH-NTRU). Then, based on the algorithm, we design the lattice-based multidimensional data privacy-preserving data aggregation scheme named FTCR-LMPPDA, in which the edge devices work as aggregator gateways. Specifically, smart meters participating in the aggregation process share zero-sum numbers to resist collusion attacks. Error retransmission mechanism and random number reconstruction algorithm are introduced to enhance the robustness of this scheme and provide our system with the ability to recover from faults. In addition, security analysis shows our scheme can resist quantum attacks, collusion attacks, and other internal and external attacks as well as keep the security features, such as confidentiality, privacy and integrity of users’ data. Finally, performance evaluation demonstrates that our scheme is more efficient than existing schemes and is more suitable for devices with constrained resources.

Ciphertext retrieval for cloud-based Internet of Things has been widely explored with the increasing popularity of cloud computing. However, in most existing solutions, the security and efficiency of the retrieval process are difficult to achieve simultaneously. To this end, we develop a novel secure matrix index, and we utilize identity-based encryption to encrypt keywords and homomorphic encryption to encrypt matrix values. The former can guarantee the security of the keyword, and the latter can realize the efficiency of the operation while ensuring safety. Then, we develop an identity-based bidirectional authentication algorithm to ensure that only authenticated users can retrieve ciphertext. In addition, we use different scoring formulas to calculate the relevance scores of documents with different lengths, ensuring that the documents are appropriately returned to users. Finally, we design a new retrieval structure to protect the privacy of the correspondence between keywords and ciphertexts. The security proof shows that the index and trapdoor can resist chosen keyword attack and keyword Guessing attack. The extensive simulation shows that our scheme is efficient.

Searchable encryption for Cloud-based Internet of Things has been widely explored with the increasing popularity of cloud computing. The public-key encryption with keyword search (PEKS) system support multiuser retrieval. However, the PEKS search time is linearly increasing as the index keyword number growth, and the search time would be huge if the index keywords consistently increase. In this article, we introduce a novel scheme named as public-key inverted-index keyword search with designated tester and multiuser key decryption (IDPEKS). First of all, we design an inverted index based on B-plus tree to reduce the search time to a logarithmic level. On this basis, we optimized the TF-IDF formula and added user preference factor and font size factor to make the relevance score calculation more consistent with needs of receiver. Besides, we design a multiuser key decryption algorithm to protect the system symmetric key. In addition, we set index server to perform the index-trapdoor retrieval process. The designated index server tester can resist the attack of the cloud server on keywords. The security proof shows that the scheme can resist the chosen keyword attack (CKA), keyword guessing attack (KGA), and key guessing attack (KeyGA). The experimental results show that the algorithm can improve retrieval efficiency while have a short encryption time.

In the pursuit of autonomous driving and intelligent traffic management, the core goal of 5G Vehicle-to-Everything (V2X) communication is to enhance the safety and efficiency of transportation systems. Modern transportation networks have evolved into 3-D structures, including bridges and tunnels from traditional 2-D ones, which poses a challenge to fixed base stations-based networks reliant on supporting continuous and seamless coverage. Against this backdrop, unmanned aerial vehicles (UAVs) play a crucial role in developing multidimensional wireless networks due to their flexibility and functionality. This article proposes a UAV-assisted vehicle authentication (UAVA) scheme. It harnesses the efficiency of edge computing and the security of zero-trust architecture, focusing on enhancing the safety and efficiency of V2X communications. The UAVA scheme employs Chebyshev chaotic mapping and elliptic curve cryptography to strengthen communication security, adapting to the dynamic interactions between vehicles and UAVs. We validate the security using BAN logic and the Scyther tool and assess performance through experiments in a real hardware environment. The results indicate that UAVA offers higher security and lower communication overhead in serverless scenarios compared to existing solutions. These comprehensive evaluations show the potential of UAVA for application in intelligent transportation systems, especially in ensuring secure communications.

Presently, the prevalent authentication approaches in smartphones are susceptible to interference from light, noise, temperature, and the risk of replay attacks. In the light of these vulnerabilities, and taking into account user behavior alongside smartphone interaction patterns, we have developed an innovative behavioral-based authentication system. This system harnesses the distinctiveness of individual keystroke dynamics for secure user authentication, offering resilience against noise and light fluctuations. In this unique approach, our smartphone’s speakers and microphones emit and capture high-frequency acoustic signals (ASs). To the best of our knowledge, this is the first instance of employing the Doppler effect generated by the high-frequency AS in response to keystroke activity as a distinctive user feature. Our definition of “keystroke behavior” encompasses the motions involved in tapping screen buttons while holding the smartphone, effectively capturing unique user attributes without necessitating any special procedures or passwords. Our initial experiments have convincingly shown that the AS Doppler effect, triggered by keystroke actions, is uniquely identifiable per user during button presses. Subsequently, we utilized a convolutional autoencoder (CAE) to distill keystroke behaviors from the reflected signals, employing an one-class support vector machine (OCSVM) for user authentication and identification processes. We then implemented a prototype of this scheme on smartphones and rigorously tested its performance across four real-world scenarios. The outcomes are promising, demonstrating that our scheme not only withstands disturbances from noise and light but also achieves an impressive average accuracy rate of 95.08%. Regarding security, it effectively thwarts replay and record attacks, further underscoring its robustness and reliability.

In Industry 4.0, artificial intelligence (AI) has been successfully applied in scenarios, such as fault prediction, traffic analysis, and production decision making. However, due to the sensitivity and security of data, privacy regulations prohibit the transfer and exchange of industrial data between entities, resulting in training data being fragmented into data silos that limit the accuracy of AI models. FL can effectively break the data silo effect, but naive federated learning (FL) (FedAvg) is vulnerable to inference attacks from aggregators and Byzantine attacks from participants. To address these issues, we propose a privacy-preserving and Byzantine-robust federated learning scheme (PBFL) for Industry 4.0. Under the setting of an benign-majority participants, PBFL can always identify benign direction and magnitude of updates. Extensive experiments demonstrate that PBFL is more robust than state-of-the-art schemes, even with extreme proportion (49%) of malicious participants. Moreover, PBFL contains a series of well-optimized 2-party computation (2PC) protocols, causing it reduces total runtime of the unoptimized implementation by around$3 \times \sim 4 \times $and$9 \times \sim 10 \times $for 32-bit and 64-bit circuits, respectively.

Considerable attention has been directed toward dynamic searchable symmetric encryption (DSSE) since it has the capability to both search and update the encrypted database. Nevertheless, the majority of DSSE schemes operate in single dimensions, and the server is assumed to be honest but curious. Consequently, there are two significant issues that must be addressed. One concerns how searchable operations can be implemented in a multidimensional space. Another is the construction of a search-result-verifiable DSSE scheme within a malicious server model. In response to the above-mentioned problems, we proposed a multidimensional verifiable DSSE scheme with volume hiding. Our design leverages the combination of various query methods across different dimensions to enable a wide array of search modes. The state chain is used in the scheme to guarantee forward privacy, while backward privacy is ensured through the encryption of the index. Our scheme employs a bitmap index of the same length to represent multiple file addresses that match a keyword, ensuring size consistency in search results, also known as volume hiding. Multiset hash function is used to construct the verifiable search result. The security of the scheme is analyzed using a simulation-based proof method based on leakage functions.

In recent years, the Internet of Things (IoT) has gained immense popularity in various aspects of work, learning, and daily life. Within the IoT realm, there is a growing concern regarding communication security issues between users and servers. However, addressing the communication security between servers is equally imperative, which has not received as much attention. To this end, we propose a certificateless anonymous authenticated key agreement (AKA) algorithm based on learning with errors (LWEs) and inhomogeneous small integer solution (ISIS) security assumptions. Our scheme provides strong resistance to quantum attacks and protects the privacy of communication servers. It also has constant communication costs and lower computational requirements than existing lattice-based anonymous AKA algorithms on the broadcast channel. Additionally, the proposed scheme eliminates the resource consumption of managing complex certificates and addresses the security risks associated with key escrow in the key generation center (KGC). Through security and performance analysis, we demonstrate that our approach can enhance the security of IoT-based healthcare systems while significantly improving communication efficiency. Our proposed scheme provides a promising solution to security issues related to server communication in IoT systems.

Notwithstanding the tremendous success of deep neural networks in a range of realms, previous studies have shown that these learning models are exposed to an inherent hazard calledadversarial example— images to which an elaborate perturbation is maliciously added could deceive a network, which entails the study of countermeasures urgently. However, existing solutions suffer from some weaknesses, e.g. parameters are usually determined empirically in some processing-based detection methods might result in a sub-optimal effect, and the directly performed processing on images might affect the classification of benign samples, leading to increment of false positive. In this paper, we propose a novel imAge-DepenDent noIse reducTION (ADDITION) model based on deep learning for adversarial detection. The ADDITION model can adaptively convert the adversarial perturbation in each image to approximate Gaussian noise by injecting image-dependent additional noise, then perform noise reduction to eliminate the adversarial perturbation, and finally detect adversarial examples by examining the classification inconsistency between the input image and its denoised version. The ADDITION model is trained end-to-end on benign samples without any prior knowledge of adversarial attacks, and thus avoid time-consuming task of generating adversarial examples in practical use. We generate more than 220,000 adversarial examples based on six attack algorithms for evaluation and present state-of-the-art comparisons on three real-word datasets. Extensive experiments demonstrate that our proposed method achieves improved performance in both detection accuracy rate and false positive rate.

Image retrieval techniques can be easily abused to violate personal privacy with images containing individuals' sensitive information. For example, people's identity information can be inferred from their face photos. Therefore, images should be sanitized before being shared or transmitted. However, previous works on image privacy protection suffer from either no provable privacy protection or poor utility with privacy guarantee. In this work, we proposeCODER, a privacy protection mechanism in image retrieval, with provable privacy guarantee as well as improved utility. In particular,CODERachieves metric differential privacy and adopts a newly proposed distortion metric definition which measures the distance more precisely to improve utility. The novel distortion metric can be applied to an arbitrary k-dimensional metric space with stronger image privacy protection. We theoretically analyze the privacy guarantee and rigorous utility bound ofCODER. We also experimentally compare its performance with two state-of-the-art works on two widely used face datasets. The results show thatCODERsignificantly improves the utility of the protected images and demonstrates its superiority in terms of the privacy-utility trade-off over the compared works. Finally, we perform reliability verification on both discriminative and generative models to demonstrate the practicality ofCODER

Network intrusion detection systems (IDS) are often considered effective to thwart cyber attacks. Currently, state-of-the-art (SOTA) IDSs are mainly based on machine learning (ML) including deep learning (DL) models, which suffer from their own security issues, especially evasion attacks by using adversarial examples. However, previous studies mostly focus on extracted features rather than the traffic sample itself, and/or assume that the adversary knows the information of the target model more or less, which severely restricts attack feasibility in practice. In this paper, we re-investigate this problem in a more realistic label-only black-box scenario and propose a practical evasion attack strategy to solve the above limitations. In this newly considered case that the adversary morphs the traffic sample and only obtains the results accepted or rejected without other knowledge, we successfully leverage the model extraction and transfer attack to evade the detection. The entire attack strategy is automated and a comprehensive evaluation is performed. Final results show that the proposed strategy effectively evades seven typical ML-based IDSs and one SOTA DL-based IDS with an average success rate of over$75\%$. We also discuss the corresponding countermeasures against our attack, which finally highlight the need for effective defenses against our attack.

Big data and bursting cloud computing technologies have facilitated an increasing trend of outsourcing data-driven services to the cloud, where the reverse kNN (RkNN) query is a popularly outsourced query service. The RkNN query aims to retrieve objects having the query object as kNN and widely applied in the product recommendation. Considering privacy concerns, the outsourced query services are demanded to protect data privacy, and consequently a series of privacy-preserving query solutions have been put forth. Nevertheless, RkNN query over high-dimensional data has not been studied to date. In this work, we design the first efficient and privacy-preserving RkNN query scheme over encrypted high-dimensional data, named PHRkNN. Specifically, we first introduce a pivot filter condition for the RkNN query and utilize it to deliberately design a pivot filter R-tree (PFR-tree) to organize the high-dimensional dataset such that the RkNN query has sublinear query efficiency. Then, we propose our PHRkNN scheme by designing some homomorphic encryption based private algorithms and applying them to privately achieve PFR-tree based RkNN query. After that, we propose an oblivious PHRkNN scheme on the basis of the PHRkNN scheme by designing a private random tree permutation (PRTP) algorithm to protect the access pattern privacy. The security of our PHRkNN scheme and oblivious PHRkNN scheme is proved by the simulation-based security analysis. The performance is verified through computational costs and communication overheads evaluation.

With the rapid advancements in machine learning and the widespread adoption of Model-as-a-Service (MaaS) platforms, there has been significant attention on convolutional neural network (CNN) inference services. However, traditional inference services over plaintext data and models are susceptible to the risks of data and model leakage. Although several privacy-preserving CNN inference schemes utilizing trusted execution environment (TEE) and cryptography have been proposed, their security models and performance still have limitations in some scenarios. Aiming at the above challenges, we present an oblivious neural network prediction scheme with semi-honest TEE, namely ToNN, which ensures the security of users’ inputs, outputs, and the model itself. Specifically, based on the limited memory of the TEE, we design secure protocols to perform CNN calculations securely and efficiently, which are friendly to support the single instruction multiple data technique. Additionally, we propose a look-up-table method to optimize the convolution and pooling layers calculations. A detailed security analysis under the simulation-based real/ideal worlds model shows that ToNN can achieve the desired security. Extensive simulation results further demonstrate that ToNN can improve the performance of linear calculations by$\textbf {4.86}\times $and non-linear calculation by$\textbf {37.68}\times $, and can be implemented effectively with low computation and communication costs.

The increasing prevalence of cloud computing drives the exploration of various secure query schemes over encrypted data, among which secure spatial keyword query has drawn a great deal of attention due to its broad application in location-based services. However, most existing schemes are either limited to the boolean keyword test or incapable of protecting access pattern privacy. Although the state-of-the-art secure spatial keyword query scheme can support keyword similarity while preserving access pattern privacy, it is unable to cope with the arbitrary spatial range, which is more general, and has limitations in efficiency and security. In this paper, we propose a new secure spatial keyword similarity query scheme that can support arbitrary spatial ranges and enhance the efficiency and security of the state-of-the-art scheme at the same time. Specifically, we first present a new homomorphic encryption technique by improving the popular symmetric homomorphic encryption (SHE). After that, we propose a novel approach to make supporting arbitrary spatial ranges over encrypted data possible, in which a spatial encoding technique is designed to improve performance. Finally, by designing a pack-based solution to protect access pattern privacy, our proposed scheme can hide the number of query results while optimizing performance. We formally prove the security of our proposed scheme and conduct experiments to evaluate its performance. The results indicate that our proposed scheme outperforms the state-of-the-art scheme in both the computational costs and communication overhead.

Outsourcing big data to cloud servers has gained prominence, and growing concerns about privacy, alongside privacy-related regulations, underscore the need to encrypt data before sending them to the cloud. Nevertheless, encryption significantly hampers the query capabilities of data, particularly in the case of vertically distributed data. This paper focuses on developing secure and efficient similarity query schemes for vertically distributed data in cloud environments. As is known, current solutions are constrained by limitations in query efficiency, approximate query results, and their ability to support vertical data. To address these issues, we introduce two novel schemes: a Fast Similarity Query Scheme (FSQ) and a Non-interactive Similarity Query Scheme (NoSQ) for outsourced distributed data. In the FSQ scheme, we enhance query efficiency by designing a trusted execution environment (TEE) assisted fast secret sharing (FSS) scheme and a series of FSS-based private algorithms, enabling secure data index construction and fast similarity query processing. For the NoSQ scheme, we eliminate communication overheads by designing a TEE assisted non-interactive secret sharing (NoSS) scheme and a series of NoSS-based private algorithms. Both schemes have undergone rigorous security validation using a simulation-based real/ideal worlds model, and their efficiency has been confirmed through comprehensive experiments.

Controller Area Network (CAN) is the most wide-used bus system in Intra-Vehicle Networks(IVN). However, the nature of broadcast communication and the lack of security mechanisms make the CAN bus extremely fragile against malicious attacks. Although there are works protecting IVN, most of them are not feasible when applied to real vehicles because they do not consider the IVN node capability. In this paper, we propose a security framework for the CAN bus, covering ECU entity identity management and authentication, symmetric key generation and update, intra-domain, cross-domain secure transmission, and sensitivity-based security classification methods. We formally verify our protocols using the up-to-date tool Tamarin and simulate real attacks in a simulation environment and the results show that the proposed protocol can resist these attacks. By the use of speck encryption and the Chaskey MAC algorithm in our schemes, the analysis results show that the increased time of a frame for a single ECU in our proposed intra-domain scheme is$2.09~ms$to$2.78~ms$on Arduino Mega, and$121.65 \mu s$to$152.15 \mu s$on Arduino DUE, which takes up$6.08\%$to$7.61\%$of a 10ms cyclic time frame. And in the cross-domain scheme is$2.55~ms$to$3.24~ms$on Arduino Mega, and$134.30 \mu s$to$164.80 \mu s$on Arduino DUE, which takes up$6.72\%$to$8.24\%$of a 10ms frame. To the best of our knowledge, this is the first time an IVN cross-domain secure transmission protocol has been proposed without changing the IVN network topology or the CAN protocol. Our work brings practical protection to IVN.

Device-to-device (D2D) communication, as a traffic offloading technology in the fifth-generation (5G) network, can be widely used in several scenarios to provide 5G characteristics of higher speed, lower latency, and larger capacity. D2D data transmission over wireless channels among mobile devices is vulnerable to security threats and privacy violations from third parties and relay nodes. However, academia and industry have yet to propose relevant schemes or standards for D2D relay data transmission scenarios. We first propose a generic construction for D2D relay communication in this paper. Then, a concrete anonymous and secure D2D data transmission scheme with trajectory tracking is presented based on Chebyshev polynomials, hash-based message authentication code, and symmetric encryption. We employ a formal verification tool -Tamarin, modal logic analysis -BAN logic, and informal security analysis to demonstrate the security features of the proposed scheme. The performance evaluation shows that the proposed scheme can achieve desirable efficiency compared with other related schemes. Finally, we developed an APP‘, D2DWatchmen’, to simulate the whole protocol and test its robustness and real execution time, where the result shows good availability and effectiveness.

Recent research has been exploring ways to streamline device pairing by introducingtouch-to-accessthat minimizes user interaction. It generates pairing keys by extracting features from a shared information source to ascertain if two devices are being held by the same person. While these solutions focus on verifying the authenticity of the device, they do not consider the legitimacy and pairing intent of the device holder. Moreover, the pairing keys exchanged over an open wireless link may be susceptible to eavesdropping attacks. In this paper, we propose a secure device pairing mechanism that utilizes the unique electrical responses of the human body to generate and transmit user-specific pairing keys, ensuring both the user's legitimacy and pairing intent while also improving key transmission reliability. We accomplish this by using the device's built-in microphone to capture ambient sound as entropy and converting it into an electrical signal transmitted by the body for device pairing. We have built a prototype and conducted extensive experiments with 31 participants to evaluate its security and usability. The results demonstrate that our proposed mechanism offers a more secure and reliable option for user-specific pairing keys, contributing to the field of device pairing.

Unmanned Aircraft Vehicles (UAVs) equipped with base stations can assist ground User Equipments (UEs) in accessing the 5G/6G network. Due to the UAV’s high autonomy, easy configuration, and strong dynamic deployment capabilities, UAV-assisted ground UEs to access the 5G/6G network can effectively expand the communication network coverage. However, some vulnerabilities exist, such as eavesdropping attack, impersonation attack, etc. In addition, the 3rd Generation Partnership Project (3GPP) committee has proposed that the UAV can employ the primary authentication mechanism (i.e., 5G-AKA) to connect to the network. Nevertheless, the primary authentication mechanism 5G-AKA has some security problems. In this paper, we first improve the existing 5G-AKA, which resists quantum attack and traceability attack and consumes moderate signaling overhead and short running time. Then, based on the improved 5G-AKA protocol, we propose a UAV-assisted UE access authentication scheme for the 5G/6G network. In the proposed scheme, the UAV can perform the service access authentication process to access the 5G/6G core network and then execute the UAV-assisted UE access authentication process to assist UE in obtaining network services. Additionally, the ground UE can perform a fast and secure handover process with the target UAV to ensure continuous network services. The automation verification tool Tamarin is employed to verify the security of the proposed scheme. Additionally, we implement the improved 5G-AKA protocol and the existing 5G-AKA protocol on Field Programmable Gate Array (FPGA) to test their running time. The security and performance evaluation results show that the proposed scheme provides robust security with moderate efficiency.

Aerial computing is gradually playing an essential role in edge and fog computing paradigms by virtue of mobility, availability, scalability, flexibility, and simultaneity, where the Low-altitude Computing (LAC) platform, as the end close to the data sources, is mainly responsible for data collection and storage. However, because of the long physical distance of data transmission and the vulnerability of the transmission link to various attacks, how to efficiently share the stored data while ensuring data privacy is a critical issue for LAC at present. In this paper, we propose a lightweight and secure private data storage and sharing scheme to support range queries over encrypted multi-dimensional data. Specifically, we first propose two data conversion methods for transforming location features and collected log files with multi-dimensional attributes in Unmanned Aerial Vehicles (UAVs). Based on the ideas of asymmetric scalar-product-preserving encryption (ASPE) and inner product comparison (IPC), we design a privacy-preserving storage and sharing technique for the converted data. In addition, to achieve secure and efficient data querying and result verification, we design a secure data index and build a data authentication structure (DAS) with G-tree. Finally, we rigorously analyze the security of our proposed scheme and conduct extensive experiments on a real-world database to prove that our proposed scheme is secure and easy to use in practical application scenarios.

With the rapid growth of Internet data volumes, Big Data analysis technologies have gradually permeated all aspects of life. However, the existence of data silos and the promulgation of relevant regulations make it challenging to apply these technologies. In this context, federated learning provides a feasible solution. Especially, XGBoost schemes for vertical federated learning have attracted much attention due to the widespread use of XGBoost. However, these schemes have limitations in terms of security or efficiency. To address these issues, we propose an efficient and privacy-preserving vertical federated learning framework based on the XGBoost algorithm, namely ELXGB, which achieves secure data alignment, XGboost training, and inference services. First, we design two node split algorithms based on homomorphic encryption and differential privacy, which securely and efficiently achieve tree node generation to construct the global model. Then, we utilize attribute obfuscation and direction obfuscation to achieve a secure inference algorithm, which avoids sensitive information leakage and protects the global model. Additionally, the global model of ELXGB is designed to be centralized, which does not require all participants to stay online for inference. Detailed security analysis demonstrates that ELXGB is privacy-preserving. Moreover, extensive experiments on real-world datasets indicate that ELXGB achieves high efficiency without sacrificing model accuracy.

The ever-growing data scale and increasingly strict privacy restraint have recently drawn extensive attention to federated learning (FL) as a multi-party machine learning paradigm for achieving high-quality model construction without data collection. Nevertheless, uploading local models in FL can still be exploited by adversaries to infer participants' sensitive data. Furthermore, it is possible for malicious participants to manipulate the global model by submitting poisonous local models. To tackle these challenges, this paper proposes an efficient and privacy-preserving federated learning framework against poisoning adversaries, namely ELFL, which can ensure the confidentiality of local models while effectively resisting data poisoning attacks. Specifically, we first design a grouped secure aggregation algorithm, through which the aggregation server can compute the summations of local models inside logic groups but cannot see individual ones. Then, based on grouped aggregations, our poisoning defense mechanism could detect and quickly phase out malicious participants from training candidates. Moreover, the computational complexity of participants is independent of their total number, so it is suitable for large-scale scenes. Detailed security analysis demonstrates the security of ELFL. Experimental results show that ELFL could maintain a high accuracy against representative data poisoning attacks, and its computational and communication overhead is indeed low.

Previous knowledge distillation methods have shown their impressive performance on model compression tasks, however, it is hard to explain how the knowledge they transferred helps to improve the performance of the student network. In this work, we focus on proposing a knowledge distillation method that has both high interpretability and competitive performance. We first revisit the structure of mainstream CNN models and reveal that possessing the capacity of identifying class discriminative regions of input is critical for CNN to perform classification. Furthermore, we demonstrate that this capacity can be obtained and enhanced by transferring class activation maps. Based on our findings, we propose class attention transfer based knowledge distillation (CAT-KD). Different from previous KD methods, we explore and present several properties of the knowledge transferred by our method, which not only improve the interpretability of CAT-KD but also contribute to a better understanding of CNN. While having high interpretability, CAT-KD achieves state-of-the-art performance on multiple benchmarks. Code is available at: https://github.com/GzyAftermath/CAT-KD.

Similarity range query over string sequences plays a significant role in bioinformatics, entity extraction, data mining and information retrieval. Meanwhile, with the popularity of cloud computing, a new paradigm is to outsource the similarity range query service to the cloud. However, existing solutions still have suffer from security and query efficiency drawbacks. To address these issues, we propose an efficient and private similarity range query scheme over encrypted string sequences. Specifically, we first organize string sequences into inverted index and design an inverted index based efficient similarity range query algorithm by applying several filter strategies. Second, based on public-key homomorphic encryption, we design a set of privacy-preserving protocols to protect the privacy of inverted index based range queries. Based on this, we propose a private and computationally efficient similarity range query scheme. Finally, we analyze the security of our scheme and conduct experiments to evaluate its performance, and the results indicate that our proposed scheme is computationally effective.

The explosive growth of data scales has recently drawn extensive attention to machine learning (ML) prediction services for achieving effective and efficient decision-making. However, the potential risk of model or data leakage, as well as vertically partitioned query data in real-world scenes, bring many challenges to ML prediction. Therefore, this paper proposes an efficient and privacy-preserving logistic regression prediction scheme, through which query institutions with vertically partitioned data can enjoy secure, fast, and high-accuracy prediction services from one cloud service provider. Specifically, we take the efficiency advantage of the trusted execution environments (TEE) technique under a semi-honest model, and design a series of masking and recovery methods based on homomorphic encryption to guarantee privacy inside and outside the enclave. Moreover, the ciphertext packing technique is integrated into our scheme for processing predictions parallelly, which further reduces communication and computational overhead. As a result, our scheme will not introduce additional strong security assumptions to TEE; meanwhile, it achieves a trade-off between security and efficiency. Based on the simulation-based paradigm, we formally prove the selective security of our scheme. Extensive experiments on real-world and synthetic datasets show that our prediction accuracy is comparable with plaintext prediction, and our scheme has at least a 30× running speedup compared to the existing representative schemes.

Model poisoning attacks greatly jeopardize the application of federated learning (FL). The effectiveness of existing defenses is susceptible to the latest model poisoning attacks, leading to a decrease in prediction accuracy. Besides, these defenses are intractable to distinguish benign outliers from malicious gradients, which further compromises the model generalization. In this work, we propose a novel defense including detection and aggregation, named RECESS, to serve as a “vaccine” for FL against model poisoning attacks. Different from the passive analysis in previous defenses, RECESS proactively queries each participating client with a delicately constructed aggregation gradient, accompanied by the detection of malicious clients according to their responses with higher accuracy. Further, RECESS adopts a newly proposed trust scoring based mechanism to robustly aggregate gradients. Rather than previous methods of scoring in each iteration, RECESS takes into account the correlation of clients’ performance over multiple iterations to estimate the trust score, bringing in a significant increase in detection fault tolerance. Finally, we extensively evaluate RECESS on typical model architectures and four datasets under various settings including white/black-box, cross-silo/device FL, etc. Experimental results show the superiority of RECESS in terms of reducing accuracy loss caused by the latest model poisoning attacks over five classic and two state-of-the-art defenses.

With the increasing severity of user privacy leaks in online social networks (OSNs), existing privacy protection technologies have difficulty meeting the diverse privacy protection needs of users. Therefore, privacy‐aware (PA) for the text data that users post on OSNs has become a current research focus. However, most existing PA algorithms for OSN users only provide the types of privacy disclosures rather than the specific locations of disclosures. Furthermore, although named entity recognition (NER) technology can extract specific locations of privacy text, it has poor recognition performance for nested and interest privacy. To address these issues, this paper proposes a PA framework based on the extraction of OSN privacy information content. The framework can automatically perceive the privacy information shared by users in OSNs and accurately locate which parts of the text are leaking sensitive information. Firstly, we combine the roformerBERT model, BI_LSTM model, and global_pointer algorithm to construct a direct privacy entity recognition (DPER) model for solving the specific privacy location recognition and entity nesting problems. Secondly, we use the roformerBERT model and UniLM framework to construct an interest privacy inference (IPI) model for interest recognition and to generate interpretable text that supports this interest. Finally, we constructed a dataset of 13,000 privacy‐containing texts for experimentation. Experimental results show that the overall accuracy of the DPER model can reach 91.80%, while that of the IPI model can reach 98.3%. Simultaneously, we compare the proposed model with recent methods. The analysis of the results indicates that the proposed model exhibits better performance than previous methods.

Modern business models improve the efficiency of supply chain management by attaching tags to products. These tagged products typically change owners multiple times during their life cycles. The ownership transfer protocol authorizes the new owner by replacing the old owner’s authentication information stored in the tag with the new owner’s. Until now, a considerable amount of literature has proposed solutions to the problem of RFID ownership transfer. Unfortunately, these existing protocols are either flawed in some security properties especially in protecting new and old owners’ privacy, or are associated with huge computational overheads. In this article, we propose an ultralightweight RFID ownership transfer protocol based on permutation function. The tag and reader only use efficient bit operations, which greatly reduce the computational overhead. An important feature of the proposed protocol is that the new owner can impose calculations on data that has been encrypted by the old owner. The new owner is authorized by the old owner and does not have access to the tag’s key, which protects the old owner’s privacy stored in the tag side. We compare our protocol with existing work, and show the advantages in terms of security, computational overhead, and time cost.

With the ubiquity of Internet of Things, location-based service (LBS) providers have collected huge volumes of individuals’ trajectories, which are valuable for some applications, e.g., store location choosing for merchants. However, directly publishing raw trajectories to applications may violate individuals’ data privacy and lead to unexpected loss. Although many trajectory synthesis methods under differential privacy have been proposed to privately publish trajectories data, they cannot sufficiently preserve the semantic information of trajectories. Aiming at this issue, in this article, we introduce a semantic-preserving scheme to synthesize trajectories for publishing under differential privacy. Specifically, we first design a hierarchical graphical model (HGM) to capture the semantic feature of trajectories. Then, we propose a metric, named the correlation score, to measure the relationship between two locations, which can well capture the geographic feature of trajectories. After that, we propose a private trajectory synthesis algorithm by first adding Laplace noises to the extracted features and then synthesizing trajectories based on the noisy features and the Markov chain theory. Privacy analysis demonstrates that our scheme can protect the privacy of trajectories. In addition, performance evaluation illustrates that our synthetic trajectories maintain good utility semantically and geographically.

The integration of satellite and terrestrial networks presents new opportunities and challenges for high speed rail (HSR) communications. Since the HSR runs vary fast, user terminals on the HSR have to perform handover authentication when the HSR passes through different terrestrial base stations or satellite coverage areas. To improve the security and robustness of HSR communication services, a fast handover authentication protocol (FHAP) for high-speed mobile terminals in the 5G satellite–terrestrial-integrated networks (STNs) is proposed. In the FHAP, user terminals in the same carriage form a temporary group managed by a relay node. The prehandover authentication mechanism is employed, in which the terrestrial 5G core network configures the preauthentication information to multiple access nodes based on the Chinese Remainder Theorem according to the location information of the HSR. Thus, group members and one of the access nodes can achieve fast handover authentication with the preconfigured information. Considering that HSR has a high running speed, when handover authentication fails, user terminals can perform handover authentication with other access nodes, and the probability of handover authentication failure caused by a single access node can be effectively reduced. We use the protocol verification tool Scyther and the Burrows–Abadi–Needham (BAN) logic to prove the security of the FHAP and compare it with other similar protocols in terms of signaling, bandwidth, and computational overhead. The analysis results show that the FHAP satisfies better security properties and has excellent performance.

With the advent of 5G and the Internet of Things, edge computing and cloud computing with their respective strengths are bound as Cloud-Edge-End Orchestrated (CEEO). The CEEO network integrates artificial intelligence and provides innovative technologies for smart grid applications and services. With massive data transmission on the CEEO network, the trustworthiness of the service node exerts an enormous influence on data privacy. To realize securely share data and decrease the local storage, end-user prefer to encrypt data and upload it to the cloud. Meanwhile, the challenge is how to balance efficiency and security perfectly when users need to find relevant documents containing specific keywords from the CEEO network. In this article, we innovatively propose a searchable encryption scheme that supports multi-keyword subset retrieval, named MSIAP. Specifically, we enhance the Apriori, a data mining algorithm, to mine the relevance of files from massive information and build a multi-level index structure. On this basis, we achieve efficient multi-keyword subset retrieval and dynamic update with insignificant information disclosure in the smart grid. Furthermore, our MSIAP strengthens the present data retrieval methods and enormously reduces the time complexity to accommodate the system of distributed smart grid. Finally, we provide the security analysis and performance evaluations by comparing them with existing works.

With the explosive growth of data volume and computing capability, federated learning, which involves constructing global models over multiple data islands, has demonstrated its advantages and vast prospects in the field of machine learning. However, due to commonly vertically partitioned data, coupled with privacy concerns about data leakage, there are still some challenging issues in traditional federated learning. To tackle these challenges, in this article, we propose an efficient and privacy-preserving vertical federated learning framework for logistic regression, named VFLR, where multiple participants can collaboratively perform global model training and query over their vertically partitioned data. Specifically, we first design a data aggregation matrix construction algorithm, with which the vertically partitioned data can be aggregated for high-accuracy global model training. Then, by utilizing a novel symmetric homomorphic encryption, our framework can ensure that the whole training and query processes do not leak any private information. Moreover, based on the data aggregation matrix, multi-round interactions are not required in VFLR, improving training efficiency significantly. Detailed security analysis shows that VFLR can well protect data and model information from inference attacks. In addition, extensive experiments demonstrate that VFLR has high training and query accuracy and low computation and communication overhead.

With the rapid development of intelligent applications, many Internet of Things (IoT) devices are deployed in various application scenarios, playing an extremely important role. Remote attestation is an important method to ensure the software integrity of these devices and protect them from several attacks. Due to the lack of security hardware and no support of hardware extensions for Class-1 IoT devices, it is particularly important to design a suitable remote attestation scheme for these devices. In this paper, we first propose the delayed observation mechanism to alleviate the problem that the software-based remote attestation scheme is not suitable for wireless networks. At the same time, we propose a ”filling memory at attestation-time” mechanism, which solves the problem that attackers hide malicious code through return-oriented programming. Finally, we introduce a reputation mechanism to assist our attestation, and adopt the principle of ”making higher-performance verification nodes take on more work” to greatly reduce the time-consuming attestation. We analyze the security of the scheme and implement it on a UNO-R3 development board to prove its practicability and effectiveness. Compared with traditional software-based attestation schemes, our scheme can reduce the attestation time and resist proxy attacks.

The equation-solving model extraction attack is an intuitively simple but devastating attack to steal confidential information of regression models through a sufficient number of queries. Complete mitigation is difficult. Thus, the development of countermeasures is focused on degrading the attack effectiveness as much as possible without losing the model utilities. We investigate a novel personalized local differential privacy mechanism to defend against the attack. We obfuscate the model by adding high-dimensional Gaussian noise on model coefficients. Our solution can adaptively produce the noise to protect the model on the fly. We thoroughly evaluate the performance of our mechanisms using real-world datasets. The experiment shows that the proposed scheme outperforms the existing differential-privacy-enabled solution, i.e., 4 times more queries are required to achieve the same attack result. We also plan to publish the relevant codes to the community for further research.

Biometric applications makes biometric authentication replace the traditional password in many cases. Biometric recognition technology has the advantages of convenience and high stability, facilitating identity recognition. However, the shortcoming of biometric authentication is easy to be stolen and leaked, which raises security concerns. In this paper, we design a lightweight joint biometric authentication scheme (SELBA) based on face and fingerprint. We improve searchable encryption (SE) to protect the privacy security of extracted biometric features in the storage and authentication stage. Because of the problem that biometric features cannot be changed or retrieved once leaked in existing schemes, we propose a cancelable mechanism to reconstruct stolen or damaged biometric templates. Moreover, we make a complete security analysis of the SELBA to meet the confidentiality, renewability, revocability, irreversibility and unlinkability of template in biometric recognition. Meanwhile, we conduct experiments on real data sets to show that SELBA is secure, efficient and easy to use in practical application scenarios.

The evolution of social media has led to a trend of posting daily photos on online Social Network Platforms (SNPs). The privacy of online photos is often protected carefully by security mechanisms. However, these mechanisms will lose effectiveness when someone spreads the photos to other platforms. In this article, we propose Go-sharing, a blockchain-based privacy-preserving framework that provides powerful dissemination control for cross-SNP photo sharing. In contrast to security mechanisms running separately in centralized servers that do not trust each other, our framework achieves consistent consensus on photo dissemination control through carefully designed smart contract-based protocols. We use these protocols to create platform-free dissemination trees for every image, providing users with complete sharing control and privacy protection. Considering the possible privacy conflicts between owners and subsequent re-posters in cross-SNP sharing, we design a dynamic privacy policy generation algorithm that maximizes the flexibility of re-posters without violating formers’ privacy. Moreover, Go-sharing also provides robust photo ownership identification mechanisms to avoid illegal reprinting. It introduces a random noise black box in a two-stage separable deep learning process to improve robustness against unpredictable manipulations. Through extensive real-world simulations, the results demonstrate the capability and effectiveness of the framework across a number of performance metrics.

Location-based services (LBSs) provide enhanced functionality of mobile applications and convenience for mobile users, which plays a more and more remarkable role in people’s daily life. In LBSs, spatial range query is an essential tool for users to find interesting points in a specific region. However, during spatial range query, it is necessary for data owners and query users to exchange their location data, and the leakage of private location information has drawn significant attention in both governmental and social aspects. Meanwhile, most existing location privacy protection schemes only focus on achieving regular geometry range query over static location datasets. In this paper, we present an efficient and privacy-preserving arbitrary polygon range query scheme, named EPAPRQ. With EPAPRQ, the arbitrary and fine-grained polygon range query can be executed over a dynamic and time-series location dataset with privacy protection. Specifically, in EPAPRQ, an arbitrary polygon range query algorithm is first introduced with sub-range query technique. Then, to protect the private location information of the data owner and query users, a series privacy-preserving data computation protocols are constructed with a symmetric homomorphic encryption algorithm, and a ciphertext-based location dataset updating strategy is also designed. Finally, we propose a double filtration method through combining the quadtree index structure and minimum bounded rectangle, which is able to greatly improve the query efficiency over ciphertexts. Detailed security analysis shows that the sensitive location information in EPAPRQ can be well protected. Furthermore, we evaluate the performance of EPAPRQ in the real map, and the results demonstrate that EPAPRQ is indeed efficient.

Aiming at balancing data privacy and availability, Google introduces the concept of federated learning, which can construct global machine learning models over multiple participants while keeping their raw data localized. However, the exchanged parameters in traditional federated learning may still reveal the data information. Meanwhile, the training data are usually partitioned vertically in real-world scenes, which causes difficulties in model construction. To tackle these problems, in this paper, we propose an efficient and privacy-preserving vertical federated tree boosting framework, namely SGBoost, where multiple participants can collaboratively perform model training and query without staying online all the time. Specifically, we first design secure bucket sharing and best split finding algorithms, with which the global tree model can be constructed over vertically partitioned data; meanwhile, the privacy of training data can be well guaranteed. Then, we design an oblivious query algorithm to utilize the trained model without leaking any query data or results. Moreover, SGBoost does not require multi-round interactions between participants, significantly improving the system efficiency. Detailed security analysis shows that SGBoost can well guarantee the privacy of raw data, weights, buckets, and split information. Extensive experiments demonstrate that SGBoost can achieve high accuracy comparable to centralized training and efficient performance.

The advance of cloud computing has driven a new paradigm of outsourcing large-scale data and data-driven services to public clouds. Due to the increased awareness of privacy protection, many studies have focused on addressing security and privacy issues in outsourced query services. Although many privacy-preserving schemes have been proposed for various query types, the set reverse k nearest neighbors (RkNN) query is still an unexplored area. Even if some existing schemes can be adapted to achieve privacy-preserving set RkNN queries, they will suffer from linear search efficiency. As a steppingstone, in this paper, we propose an efficient and privacy-preserving set RkNN query scheme over encrypted data with sublinear query efficiency. Specifically, we first design an inverted prefix index to organize the set dataset and propose an algorithm to traverse the index with sublinear search efficiency. Then, we propose two oblivious data comparison protocols based on a symmetric homomorphic encryption (SHE) scheme and design the private filter/refinement protocols to preserve the privacy of index searching. After that, we propose an access pattern privacy-preserving set RkNN query scheme by using private filter/refinement protocols. Rigorous security analysis demonstrates that our scheme can protect data privacy and access pattern privacy. Experimental results indicate that our scheme is more efficient than the available naive solution in terms of computational costs and communication overheads.

The boom of cloud computing has stimulated the prevalence of outsourced query services, and privacy concerns further motivate extensive studies on privacy-preserving queries in the cloud. Graph similarity query is one critical query type, in which the similarity between two graphs is usually measured by graph edit distance (GED). Although many schemes have been proposed for GED computation/graph similarity query, they do not consider data privacy and are not applicable to the cloud computing scenario. To address this issue, in this paper, we propose the first efficient and privacy-preserving graph similarity query (PGSim) scheme in the filter and verification framework. Specifically, we first identify the pivot filter property of GED and use the property to design a pivot R-tree based filter algorithm, which can efficiently retrieve candidate graphs for graph similarity query. Then, we design a vertex mapping (VM) tree to index all vertex mappings between two graphs and develop a GED query verification algorithm to verify candidate graphs. After that, we design a suite of private algorithms based on a symmetric homomorphic encryption scheme and apply them to propose a pivot R-tree based filter predicate encryption (PRFilter) scheme and a private GED query verification (PGQVerify) algorithm. Based on the PRFilter scheme and the PGQVerify algorithm, we propose our PGSim scheme. Rigorous security analysis shows that our scheme is selectively secure. Performance evaluation also demonstrates the high efficiency of our scheme.

The aggregated reverse kNN (ARkNN) query aims to identify one query record with the maximum influence set and has become a powerful tool to support optimal decision-making in crowdsensing. Considering data privacy and query privacy, ARkNN queries should be performed in a private manner. Unfortunately, existing schemes cannot support privacy-preserving ARkNN queries over crowd-sensed data. To address this issue, we propose two efficient and privacy-preserving ARkNN query schemes with different security levels, named the BARQ scheme and the EARQ scheme, where the former can only protect data privacy while the latter can protect both data privacy and query privacy. Specifically, we first formalize the models of privacy-preserving ARkNN queries and propose our BARQ scheme based on a random response (RR) frequency oracle. Then, we design a privacy-preserving hardware-assisted reverse kNN query determination (PRkD) scheme for privately determining whether a query record is among the RkNN of a data record. After that, we present our EARQ scheme by leveraging the PRkD scheme to protect query privacy and integrating the RR frequency oracle to protect data privacy. In addition, our rigorous security analysis demonstrates that the BARQ scheme can well protect data privacy, and the EARQ scheme can protect both data privacy and query privacy. Extensive experimental results illustrate that they have high accuracy in query results and are efficient in computational costs and communication overheads.

As an outlook of the terrestrial network, the flexible Unmanned Aerial Vehicle (UAV)-aided satellite-terrestrial integration network would have numerous prospective applications such as service enhancement, maritime communication, military, and emergency communication, which is getting significant attention. However, the public and open-access network must result in various imperative risks including impersonation, sensitive data, and privacy disclosure. Due to several unique characteristics including long transmission distance, unstable communication environment, resource-limited and highly dynamic characteristics of UAVs, diversified terminals that lack the ability of accessing the satellite and may be resource-limited, there are new challenges for the access authentication process in the UAV-Aided Satellite-Terrestrial Integration Networks with 5G. In this paper, we present a novel Physically Unclonable Function (PUF)-based access authentication scheme consisting of two access authentication protocols for the UAV and the ground terminals like 5G User Equipments (UEs), respectively. Two access authentication protocols for the drone and the terminals can both achieve mutual authentication, key agreement, and privacy protection with distinct advantages of no need to store secret key and supporting physical attack resistance. Finally, we propose an efficient handover authentication protocol executed by the ground terminals when the UAV is required to switch. We employ different security analysis tools to analyze the security of all proposed protocols, as well as present informal security analysis on various security properties. The performance comparison and evaluations show our protocols have better advantages.

The advance of artificial intelligence has stimulated the rapid development of multi-biometric recognition that recognizes users based on their multiple biometric features. Since biometric features are private information of users, multi-biometric recognition has to be processed in a privacy-preserving way. Although many approaches have been proposed for privacy-preserving multi-biometric recognition, they have some limitations in security and performance. Aiming at this challenge, we propose a privacy-preserving multi-biometric recognition scheme. In this scheme, we first utilize a neural network to train a face and voiceprint fusion model, which obtains better fusion layer parameters and improves the accuracy of fusion recognition. Then, we design an efficient and privacy-preserving multi-biometric recognition scheme based on the MK-CKKS cryptosystem in the decentralized model, which does not rely on a trusted third party to increase its universality. The scheme can guarante recognition accuracy by utilizing a feature-level fusion mechanism, reaching EERs as low as 0.66%. Security analysis shows that the privacy of biometric information and recognition results can be guaranteed. Experimental results validates the efficiency of our scheme.

Recently users’ right-to-be-forgotten is stipulated by many laws and regulations. However, only removing the data from the dataset is not enough, as machine learning models would memorize the training data once the data is involved in model training, increasing the risk of exposing users’ privacy. To solve this problem, currently, the straightforward method, naive retraining, is to discard these data and retrain the model from scratch, which is reliable but brings much computational and time overhead. In this paper, we propose an exact unlearning architecture called ARCANE. Based on ensemble learning, we transform the naive retraining into multiple one-class classification tasks to reduce retraining cost while ensuring model performance, especially in the case of a large number of unlearning requests not considered by previous works. Then we further introduce data preprocessing methods to reduce the retraining overhead and speed up the unlearning, which includes representative data selection for redundancy removal, training state saving to reuse previous calculation results, and sorting to cope with unlearning requests of different distributions. We extensively evaluate ARCANE on three typical datasets with three common model architectures. Experiment results show the effectiveness and superiority of ARCANE over both the naive retraining and the state-of-the-art method in terms of model performance and unlearning speed.

Recent efforts in reducing user involvement during device pairing have successfully introduced touch-to-access. To detect whether two devices are being held by the same person, existing touch-to-access solutions extract features from a shared information source to generate pairing keys. They focus on validating the device's authenticity by only requiring the user's simple touching of the device, however, ignore the device holder's legitimacy and pairing intent. Moreover, the pairing keys may be vulnerable to eavesdropping attacks since they are exchanged over an open wireless link (e.g., WiFi or Bluetooth). In this paper, we develop a secure device pairing mechanism that essentially uses the human body to generate and transmit user-specific pairing keys, ensuring the user's legitimacy and pairing intent, as well as improving key transmission reliability. Our work is based on the observation that the human body produces a unique response to the electrical signal flowing through it, and different bodies induce distinct responses to the signal. The built-in microphone on devices captures ambient sound as an entropy source and converts it into an electrical signal, which is subsequently processed and transmitted by the human body for device pairing. We build a prototype using off-the-shelf microphones and conduct extensive experiments with 31 participants to evaluate its security performance and usability. The results show that our system achieves a pairing success rate of 97.74% and an equal error rate of 2.28%.

We present a secure device pairing approach, called BiTouch, using the human body as a conductor to generate and transmit user-specific pairing keys for advancing touch-to-access policy. BiTouch is designed based on the observation that the human body responds uniquely to electrical signals flowing through it. Built-in microphones on devices are essentially used to capture ambient sound as entropy and convert it into an electrical signal, which is subsequently transmitted by the body for device pairing. We implement BiTouch using off-the-shelf microphones and evaluate it with 31 participants. The results demonstrate that BiTouch ensures the user's legitimacy and key transmission reliability, and achieves a pairing success rate of 97.74% and an equal error rate of 2.28%.

The smart city is a vision for urban development that combines new-generation information technologies to improve the quality of life. The Internet of Things (IoT) technology is a key technology of smart city, which provides the support for the automatic collection and transmission of massive information in smart city. However, there are some challenges for the IoT devices in a smart city in terms of security and performance, such as various security vulnerabilities and a large amount of computational overhead. With the explosive growth of the number of IoT devices and the data they generate, it is very necessary to ensure that a large number of IoT devices can establish secure and reliable communication with the central server. In this article, we design a massive IoT device remote identity attestation protocol based on the reputation mechanism and physically unclonable function (PUF). Our protocol can efficiently and securely accomplish the mutual authentication and key agreement between massive IoT devices and central server, and between IoT devices. The Burrows–Abadi–Needham (BAN) logic and a formal verification tool called Scyther are employed to prove the security of our protocol. We also evaluate the performance by comparing our protocol with other related protocols in terms of computational overhead, communication overhead, etc. The security and performance results show that our protocol has ideal security and performance.

With the gradual commercialization of the fifth-generation (5G) network, the narrowband Internet of Things (NB-IoT) system would have potential and prospective applications in future relying on the infrastructure. Meanwhile, predictably, there are several security requirements to be satisfied, including concurrent access authentication for massive devices, identity privacy protection, physical attack resistance, application traffic security, etc. In this article, we present a novel group authentication and data transmission scheme using the physically unclonable function (PUF) for NB-IoT in which the output of PUF is viewed as shared root key to achieve the mutual authentication along with key agreement. By this scheme, a Group Leader is employed to aggregate and relay authentication information and, thus, it reduces the signaling cost and communication cost followed by activating attach request messages from a sea of devices. The network side as well can surely find fake ones through individual truncated authentication code and detect honest devices with high probability when aggregated authentication code is invalid. Furthermore, the revised security model and the formal verification tool Scyther are employed to evaluate the security of the scheme. Finally, performance analysis results show that our solution has the desired efficiency.

As a straightforward consequence of advances in the Internet of Things (IoT), location-based service (LBS) applications have been pervasive in our daily lives. Nevertheless, since those LBS applications will continuously collect and disclose users’ location data, major concerns on privacy leakage are raised. Aiming at the challenge, in this article, we first build up a detect module (DM) and employ it to investigate more than 80% of LBS applications are keen on tracking users. Then, to thwart the threats from those LBS applications, we exploit the deceptive dummy techniques and design a dummy-based location privacy preserving scheme, named dummy location provider (DLP), which comprises three algorithms, namely, Spread, Shift, and Switch. Specifically, Spread and Shift are in charge of generating deceptive dummies and trajectories. And with Switch, users’ real locations are replaced with dummy trajectories before being submitted to LBS applications. As a result, users can not only prevent applications from accessing location data arbitrarily, but also avoid being questioned by applications in terms of honesty. Furthermore, to guarantee necessary functions of LBS, DLP offers customizable privacy-preserving strategies for users, which can achieve flexible location data usage control. Finally, our DLP can also attain achievable and effortless deployment over smart devices. Detailed security analysis indicates that DLP resists inference attacks even facing skeptical applications. In addition, for performance evaluation, a DLP application (DLPA) is developed on the Android platform and tested in the real environment, and the extensive experimental results demonstrate that the DLPA is indeed effective and high efficiency in practice.

As a smart transportation application of the Internet of Things, the Internet of Vehicles (IoV) depresses the chances of traffic accidents, while improving transportation efficiency and user driving experience. However, as the number of vehicles continues to grow, the original ground-based IoV system is difficult to meet the ever-increasing demand. To this end, space–air–ground-integrated network (SAGIN) incorporates satellite systems, aerial network and terrestrial communications. However, because SAGIN integrates multiple network services and communication modes, which makes SAGIN more vulnerable to various types of attacks and security threats. This article first presents the dominating security threats in data storage, transmission and sharing of space–air–ground integrated vehicular network (SAGIVN). Moreover, for guaranteeing the safety and effectiveness of the model, we advance a safe and effective encrypted data retrieval and sharing scheme in SAGIVN (ERDSS) for possible threats, the ERDSS can execute fuzzy retrieval over misspelling keywords and sort results by relevance scores to realize precise retrieval. We perform a comprehensive security discussion and execute experiments based on real-world data sets. The consequences demonstrate that the ERDSS is safe and efficient.

Research on mobile cyber–physical systems (MCPSs) that have the superiorities of cyber–physical systems (CPSs) and expand their application range has become a trend in recent years. The applications of MCPS in fields, such as intelligent transportation systems, smart home appliances, and mobile education, have also become increasingly mature. However, MCPS also has some shortcomings that need to be solved urgently. Sensors carried on mobile devices collect data and upload huge amounts of data to the cloud for statistics and analysis. Mobile devices need to directly interact with the cloud, causing both parties to bear huge computing and communication costs. Since the interaction process includes data sharing and storage, it is particularly important to protect the data itself and the security of sharing. Searchable encryption ensures the safety of communication among the MPEs and the cloud, while protecting the privacy of data on the cloud. However, the existing searchable encryption technology cannot provide efficient and reliable data storage and sharing services for MPEs in MCPS under the premise of ensuring security. In this article, we present a secure and efficient data sharing and privacy protection scheme in MCPS on the basis of the edge computing model (NESPS). Meanwhile, the introduction of edge computing (EC) significantly reduces the communication consumption between the device and the cloud. Furthermore, attribute-based encryption (ABE) enables the NESPS to achieve fine-grained management of device authorities. Moreover, we have carried out security analysis and simulation on the NESPS, the results demonstrate that the NESPS meets the proposed requirements.

With the popularity of intelligent terminals and the advances of mobile Internet, carpooling service, which reduces the travel cost of each user by allowing multiple users to share one car, has received considerable attention and makes our life more convenient. However, the vigorous development of carpooling services still faces severe challenges in users’ location or route privacy. In this article, we propose an efficient and privacy-preserving route matching scheme called TAROT for carpooling services. With TAROT, users can enjoy high-quality carpooling services while without revealing sensitive location and route information. Specifically, based on a Goldwasser–Micali-based equality determination algorithm (GMEDA), we design an accurate similarity computation algorithm (ASCA), which allows users to get accurate carpooling results over ciphertexts. Meanwhile, the reverse Minhash (RM) method is also designed to construct a dissimilar route filter algorithm (DRFA), which can filter out dissimilar routes in advance and reduce computational costs and communication overheads. Security analysis shows that TAROT can protect users’ location privacy. In addition, TAROT is also evaluated with many random maps, and the simulation results demonstrate that TAROT is highly efficient.

Internet of Things (IoT) devices generate a massive quantity of network traffic every moment, which undoubtedly poses an urgent demand for an accurate and efficient network protocol analysis tool in cyberspace management and security. However, the existing popular methods have limitations, such as incomplete functionality and insufficient accuracy. For example, the large number of novel network applications brings unprecedented protocols for protocol analysis, which greatly limit the analysis capabilities of existing tools. In this article, we devise an automated protocol analysis framework for IoT devices calledMARSto solve these problems. To the best of our knowledge, this is the first unified framework including all three analysis stages: 1) classifying protocol; 2) analyzing the protocol phase; and 3) parsing the protocol field. At each stage, we provide effective solutions to solve the corresponding tasks and improve the efficiency of protocol analysis.MARScan also deal with unknown protocols that are common in IoT scenarios but are rarely concerned by previous works. Finally, we develop a distributed computing engine to ensure the high throughput and processing speed of the whole framework for the huge amount of network traffic. The evaluation on a variety of different protocols shows the superiority of ourMARSover previous works in terms of comprehensiveness and accuracy.

Device-to-device (D2D) communication as direct communication technology has many application scenarios and plays a very important role in the fifth-generation (5G) era. Using D2D communication in third generation partnership project (3GPP) 5G Heterogeneous Network (HetNet) can effectively relieve the network traffic pressure and reduce the energy consumption of the base station. However, there are numerous security threats in D2D applications since the D2D communication remains in the early stage. The existing standards and solutions rarely consider device discovery, efficient authentication, mutual authentication, and key negotiation with privacy protection for D2D user equipment (UE) in heterogeneous access scenarios. In this article, we present a unified efficient anonymity proximity device discovery and batch authentication mechanism for heterogeneous D2D UEs based on a new proposed efficient pairing-free certificateless batch signature (CLBS), the identity-based prefix encryption and Chinese remainder theorem (CRT). Our proposed scheme can be applied to all the 5G heterogeneous access scenarios of D2D communication. The security analysis and performance results show that our scheme can achieve mutual authentication, key agreement, identity privacy protection, batch verification, and resist several protocol attacks with ideal efficiency.

It is believed that online disease risk assessment system has great potential to alleviate the medical treatment problems for the future smart city and communities, as it can excavate disease risk factors from a large number of patient features, provide diagnostic references for doctors, and save medical treatment time for patients. However, the flourish of online disease risk assessment service still faces severe challenges including information privacy and security. In this article, based on the naïve Bayesian classification, we propose an efficient and privacy-preserving diseaserisk assessment scheme over multi-outsourced vertical datasets, named CARER. With CARER, the e-healthcare provider can securely train a disease risk predication model over vertically distributed medical data from multiple medical centers (i.e., hospitals), and provide privacy-preserving disease risk predication services for users (i.e., patients and doctors). During the model training and disease risk prediction phases, all sensitive data are operated over ciphertexts without decryption. As a result, the private information of medical centers, e-healthcare provider, and users can be well protected. Detailed security analysis shows that CARER can resist various known security threats. In addition, we evaluate the performance of CARER with real medical datasets, and the results demonstrate that CARER is efficient.

Public intelligent services enabled by machine learning algorithms are vulnerable to model extraction attacks that can steal confidential information of the learning models through public queries. Though there are some protection options such as differential privacy (DP) and monitoring, which are considered promising techniques to mitigate this attack, we still find that the vulnerability persists. In this article, we propose an adaptivequery-flooding parameter duplication(QPD) attack. The adversary can infer the model information with black-box access and no prior knowledge of any model parameters or training data via QPD. We also develop a defense strategy using DP called monitoring-based DP (MDP) against this new attack. In MDP, we first propose a novel real-timemodel extraction status assessmentscheme calledMonitorto evaluate the situation of the model. Then, we design a method to guide the differential privacy budget allocation called APBA adaptively. Finally, all DP-based defenses with MDP could dynamically adjust the amount of noise added in the model response according to the result fromMonitorand effectively defends the QPD attack. Furthermore, we thoroughly evaluate and compare the QPD attack and MDP defense performance on real-world models with DP and monitoring protection.

Over the past years, the increasingly severe data island problem has spawned an emerging distributed deep learning framework—federated learning, in which the global model can be constructed over multiple participants without directly sharing their raw data. Despite its promising prospect, there are still many security challenges in federated learning, such as privacy preservation and integrity verification. Furthermore, federated learning is usually performed with the assistance of a center, which is prone to cause trust worries and communicational bottlenecks. To tackle these challenges, in this paper, we propose a privacy-preserving and verifiable decentralized federated learning framework, named PVD-FL, which can achieve secure deep learning model training under a decentralized architecture. Specifically, we first design an efficient and verifiable cipher-based matrix multiplication (EVCM) algorithm to execute the most basic calculation in deep learning. Then, by employing EVCM, we design a suite of decentralized algorithms to construct the PVD-FL framework, which ensures the confidentiality of both global model and local update and the verification of every training step. Detailed security analysis shows that PVD-FL can well protect privacy against various inference attacks and guarantee training integrity. In addition, the extensive experiments on real-world datasets also demonstrate that PVD-FL can achieve lossless accuracy and practical performance.

5G Vehicular to everything (5G-V2X) has grown more vigorously than ever before, and services massive vehicles can obtain provided by content providers via the network in all fields are widely acknowledged, such as listening to news channels and traffic channels. In the 5G-V2X network, content providers can provide service messages to a group of vehicles belonging to a specific area in point-to-multipoint transmission mode. However, various challenges are in front of the way when vehicles obtain multicast services due to the unique features of 5G-V2X, such as massive vehicles and strong mobility. In this paper, we present a typical vehicle multicast service model in 5G-V2X and propose a group-based multicast service authentication and data transmission scheme based on this model. In the scheme, massive vehicles within the same RAN coverage are constructed into a group and connected to the content provider to gain access to a multicast service using the distributed keys securely by the 5G home network. Subsequently, the multicast service key for protecting the multicast service data is distributed to each vehicle so that the multicast service data can be securely transmitted to vehicles in point-to-multipoint mode. The security analysis results using the formal verification tool and informal security analysis show that the proposed scheme supports the multicast services authentication and authorization, multicast service data protection, key distribution protection, anonymity, unlinkability, and protocol attack resistance. The performance analysis results regarding signaling, computational and communication overheads show that the proposed scheme outperforms other related schemes.

Mobile crowdsensing has emerged as a promising paradigm where location-based sensing tasks are outsourced to mobile participants carrying sensor-equipped devices. A critical issue of crowdsensing is to guarantee the sensing coverage by appropriately recruiting participants, which requires participants’ precise locations and thus raises privacy concerns. In this paper, we are motivated to develop a privacy preserving participant recruitment scheme for mobile crowdsensing, which maximizes the spatial coverage of the sensing range while protecting participants’ location privacy against an untrusted crowdsensing platform. Briefly, we propose a utility-assured location obfuscation mechanism operated in a hexagonal grid system, which the participants can follow to locally perturb their locations with personalized privacy demands. Given the obfuscated locations, we efficiently solve a coverage-maximized participant recruitment problem with the budget constraint by using a deterministic rounding algorithm. Considering the existence of biased sensing data incurred by location obfuscation, we further develop a fault-aware crowdsensing framework to improve the robustness of the recruitment strategy, where a constant-approximation algorithm is applied to select participants against any number of unqualified sensing results. Extensive simulations on real-world location datasets and Uber’s geospatial indexing system validate the efficacy of our location obfuscation mechanism and participant recruitment schemes in mobile crowdsensing systems.

Mechanisms built upon geo-indistinguishability render location privacy, where a user can submit obfuscated locations to Location-Based Service providers but still be able to correctly utilize services. However, these mechanisms are vulnerable under inference attacks. Particularly, with background knowledge of a user’s obfuscated locations, an attacker can infer actual locations by carrying out long-term observation attacks. Unfortunately, how to defend long-term observation attacks in the field of differential location privacy remains open. In this paper, we first demonstrate the vulnerabilities of existing mechanisms under long-term observation attacks. In light of these vulnerabilities, we devise a novel mechanism, referred to as Eclipse, which bridges the gap between location protection and usability of services. Specifically, we harness geo-indistinguishability and$k$-anonymity to obfuscate locations and hide each location based on an anonymity set. As a result, our mechanism effectively perturbs the distribution of locations and suppresses leakage under long-term observation attacks. Moreover, the set of possible outputs is utilized to minimize the impacts to usability and correctness. We formally define and rigorously prove the security of the proposed mechanism by leveraging differential privacy. Moreover, we implement the proposed mechanism and conduct a series of experiments on real-world datasets to demonstrate its efficacy and efficiency.

Location privacy has been one of the most important research areas over recent years, and many location Privacy Preserving Mechanisms (PPMs) have been proposed. Each PPM typically achieves certain tradeoffs between privacy protection and resource consumption, and no PPM performs perfectly in all cases. Instead of designing one PPM that works for all cases, this paper studies how to make the best use of multiple single PPMs for location privacy protection in different scenarios. In particular, we propose a general framework called SmartGuard, which dynamically selects the best privacy preservation strategy for a user based on her preferences and the current status of her mobile device. SmartGuard quantifies user privacy under various scenarios, models the effects of different PPMs on several key factors such as the remaining battery level and network bandwidth, and then recommends the best privacy strategy for the user. To illustrate how our SmartGuard works, we apply it to a specific scenario of LBSs and implement it on Android based phones. Evaluation results show that our solution outperforms existing PPMs under various scenarios.

Recent advances in machine learning, wireless communication, and mobile hardware technologies promisingly enable federated learning (FL) over massive mobile edge devices, which opens new horizons for numerous intelligent mobile applications. Despite the potential benefits, FL imposes huge communication and computation burdens on participating devices due to periodical global synchronization and continuous local training, raising great challenges to battery constrained mobile devices. In this work, we target at improving the energy efficiency of FL over mobile edge networks to accommodate heterogeneous participating devices without sacrificing the learning performance. To this end, we develop a convergence-guaranteed FL algorithm enabling flexible communication compression. Guided by the derived convergence bound, we design a compression control scheme to balance the energy consumption of local computing (i.e., "working") and wireless communication (i.e., "talking") from the long-term learning perspective. In particular, the compression parameters are elaborately chosen for FL participants adapting to their computing and communication environments. Extensive simulations are conducted using various datasets to validate our theoretical analysis, and the results also demonstrate the efficacy of the proposed scheme in energy saving.

Abstract With the rapid development of the IoT, smart home plays an increasingly important role in daily life. At the same time, privacy issues have gradually aroused the concern of researchers. Past research proves that there are privacy risks when people use smart home devices and some protection methods are proposed to protect people's privacy. However, attackers can still infer people's behaviour and activities through analysing the wireless traffic of smart home devices. Herein, an attack method under complicated WiFi scenarios is demonstrated, including different locations, different buildings and different networks. In addition, a new protection method, Air‐Padding, is developed, which can change the traffic patterns of smart home devices through injecting constructed packets into the link between devices and routers. The results prove that Air‐Padding can prevent people's behaviour and activities from being analysed and inferred.

Multiaccess edge computing (MEC) empowers service providers (SPs) to run applications on the shared edge platforms in close proximity to mobile users, enabling ultralow latency access to a wide variety of cloud services. However, how to decide the amount of edge computing resources to rent for mobile service provisioning poses great challenges as the service demand is unknown to SPs a priori and may vary across the geographically distributed edge sites spatially and temporally. The resource rental decision also significantly affects SPs' deploying profits since it is critical for service deployment and workload assignment. This article investigates the service provisioning problem in a cooperative edge computing system under service demand uncertainty. We develop a holistic solution to make two-timescale decisions on edge resource rental and workload assignment to maximize SP's deploying profits. Briefly, we exploit historical service demand traces at the edge sites to characterize the uncertainty in a data-driven manner and formulate the edge service provisioning problem into a two-stage risk-averse optimization. To solve the formulated problem without compromising the data privacy, we propose an algorithm integrating Benders decomposition (BD) and alternating direction method of multipliers (ADMMs), which enables each edge site to keep the historical traces locally and participate in the optimization process. Based on real-world data sets, extensive simulations are conducted to validate the efficacy of our scheme.

The fifth-generation (5G) mobile communication technology has already deployed commercially and become a global research focus. The new features of 5G include unlimited information exchange, a large variety of connections with independent energy, and diversified high transmission rate services. Collective synergy of services is expected to change the way of life and future generations and introduce new converged services to the ICT industry. Different application services have to meet differentiated security demands. From the perspective of security, in order to support the multiservice of 5G services, it is necessary to consider the new security mechanism driven by the service. Based on 5G massive data stream, the 5G system can provide customized real-world services for potential users and reduce the user experience gap in different scenarios. However, 3GPP Extensible Authentication Protocol (EAP), which is the present entity authentication mechanism for the 5G service layer, is only an individual authentication architecture and unable to fulfill the flexible security objectives of differentiated services. In this paper, we present a new hierarchical identity management framework as well as an adaptable and composable three-factor authentication and session key agreement protocol for different applications in 5G multiservice systems. Finally, we propose an authorization process by combining with the proposed three-factor authentication mechanism and Service-Based Architecture (SBA) proposed by the 3GPP committee. The proposed mechanism can concurrently provide diverse identity authentication schemes corresponding to four different security levels by easily splitting or assembling three-factor authentication protocol blocks. The proposed scheme can be simultaneously applied to a variety of applications to improve the efficiency and quality of service and reduce the complexity of the whole 5G multiservice system, instead of designing or adopting several different authentication protocols. The performance evaluation results indicate that the proposed scheme can guarantee the multiple security of the system with ideal efficiency.

With the rapid growth of data, limited by the storage capacity, more and more IoT applications choose to outsource data to Cloud Service Providers (CSPs). But, in such scenarios, outsourced data in cloud storage can be easily corrupted and difficult to be found in time, which brings about potential security issues. Thus, Provable Data Possession (PDP) protocol has been extensively researched due to its capability of supporting efficient audit for outsourced data in cloud. However, most PDP schemes require the Third-Party Auditor (TPA) to audit data for Data Owners (DOs), which requires the TPA to be trustworthy and fair. To eliminate the TPA, we present a Public Mutual Audit Blockchain (PMAB) for outsourced data in cloud storage. We first propose an audit chain architecture based on Ouroboros and an incentive mechanism based on credit to allow CSPs to audit each other mutually with anticollusion (any CSP is not willing to help other CSPs conceal data problems). Then, we design an audit protocol to achieve public audit efficiently with low cost of audit verification. Rigorous analysis explains the security of PMAB using game theory, and performance analysis shows the efficiency of PMAB using the real-world dataset.

With the pervasiveness of mobile devices and the development of biometric technology, biometric identification, which can achieve individual authentication relies on personal biological or behavioral characteristics, has attracted widely considerable interest. However, privacy issues of biometric data bring out increasing concerns due to the highly sensitivity of biometric data. Aiming at this challenge, in this paper, we present a novel privacy-preserving online fingerprint authentication scheme, named e-Finga, over encrypted outsourced data. In the proposed e-Finga scheme, the user's fingerprint registered in trust authority can be outsourced to different servers with user's authorization, and secure, accurate and efficient authentication service can be provided without the leakage of fingerprint information. Specifically, an improved homomorphic encryption technology for secure euclidean distance calculation to achieve an efficient online fingerprint matching algorithm over encrypted FingerCode data in the outsourcing scenarios. Through detailed security analysis, we show that e-Finga can resist various security threats. In addition, we implement e-Finga over a workstation with a real fingerprint database, and extensive simulation results demonstrate that the proposed e-Finga scheme can serve efficient and accurate online fingerprint authentication.

Ultra-dense Heterogeneous network (HetNet) technique can significantly improve wireless link quality, spectrum efficiency and system capacity, and satisfy different requirements for coverage in hotspots, which has been viewed as one of the key technologies in fifth Generation (5G) network. Due to the existence of many different types of base stations (BSs) and the complexity of the network topology in the 5G HetNets, there are a lot of new challenges in security and mobility management aspects for this multi-tier 5G architecture including insecure access points and potential frequent handovers among several different types of base stations. In this paper, we integrate user capability and Software Defined Network (SDN) technique, and propose a capability-based privacy protection handover authentication mechanism in SDN-based 5G HetNets. Our proposed scheme can achieve the mutual authentication and key agreement between User Equipments (UEs) and BSs in 5G HetNets at the same time largely reduce the authentication handover cost. We demonstrate that our proposed scheme indeed can provide robust security protection by employing several security analysis methods including the BAN logic and the formal verification tool Scyther. In addition, the performance evaluation results show that our scheme outperforms other existing schemes.

Many artificial intelligence technologies have been applied for extracting useful information from massive industrial big data. However, the privacy issues are usually overlooked in many existing methods. In this article, we propose an anonymous and privacy-preserving federated learning scheme for the mining of industrial big data. We explored the effect of the proportion of shared parameters on the accuracy through experiments, and found that sharing partial parameters can almost achieve the accuracy of sharing all the parameters. On this basis, our proposed federated learning scheme reduces the privacy leakage by sharing fewer parameters between the server and each participant. Specifically, we leverage differential privacy on shared parameters with Gaussian mechanism to provide strict privacy preservation; the effect of different ε and δ on accuracy is tested; and we keep track of δ-when it reaches a certain threshold, training shall be stopped. What's more, we employ a proxy server as the middle layer between the server and all the participants to achieve anonymity of participants; it is worth noting that this can also reduce the communication burden on the federated learning server. Finally, we provide the security analysis and performance evaluations by comparing with other schemes.

Wireless networks are usually deployed to cover more overlapping areas, experiencing severe interferences and hence making interference management essential for their viability. Interference alignment (IA) is an effective way of interference management and has thus received significant attention. With IA, at least one degree-of-freedom (DoF) should be used to place the aligned interferences. However, when multiple interferences are from one identical transmitter and to a common destination, IA is not applicable under a one-DoF cost constraint. If these interfering signals are aligned in the same direction at the interfered receiver, they will also overlap with each other at their intended receiver, thus becoming indistinguishable. Moreover, IA is realized by adjusting the spatial feature of disturbance, hence incurring co-channel interference to the interfering transmission-pair’s own communication. To solve this problem, we proposecooperative IA. Instead of adjusting the spatial characteristics of all interferences, we aim to achieve IA by adjusting interferences’ strength along with one or none interference’s signature, based on the interactions among multiple wireless disturbances. We propose two cooperative IA schemes: cooperative IA with space-power adjustment and cooperative IA with power adjustment. With the first method, the overall effect of all interferences is aligned in the orthogonal direction with respect to the desired signal at the interfered receiver. This method modifies the direction of one interference with a properly designed precoding vector and the strength of all interferences via power allocation. Under the second scheme, the strength of all interfering signals is modified so that the overall effect of multiple interferences is orthogonal to the desired transmission while guaranteeing the orthogonality among the interfering signals at their intended receiver. Our theoretical analysis and in-depth simulation have shown that the proposed schemes can effectively manage multiple interferences from an identical source while achieving good performance of the interfering transmission-pair.

The platoon-based driving pattern, a cooperative driving pattern for a leader and a number of followers, is a good way to deal with some traditional traffic issues and brings many benefits when performing special platoon tasks. However, it's possible for each platoon member to be fully controlled by attackers, especially the leader, which may cause severe damage to the platoon stability. In this paper, we propose a vehicle platoon leader updating scheme in vehicular networks based on blockchain techniques and reputation management mechanism, so as to ensure that the most trusted platoon member acts as leader. In this scheme, according to received warning messages of traffic events, each platoon member evaluates others' reputations in the form of offsets. Using the designed reputation blockchain with Delegated Proof-of-Stake (DPoS) consensus scheme, miners generate blocks in turn and wait for others' verification. If a block successfully passes the consensus process, it will be formally added to the blockchain, which can reflect all platoon members' current reputation values. In addition, we also have to guarantee that the miner group is composed of several platoon members with high reputation and the leader serves as a miner. In this way, the scheme both meets the real-time requirement of reputation management and saves communication overhead compared with existing schemes. Finally, we analyze the proper functioning and security of our proposed scheme.

Anomaly detection is a typical method to find abnormal behaviors in smart grid, where the data may contain both categorical and numerical attributes with distinct significance. The k-prototypes algorithm is one of the most common algorithms for clustering mixed categorical and numerical data, however, it does not consider the significance of different attributes towards the clustering process. In this paper, we propose a weight based k-prototypes algorithm for anomaly detection in smart grid. Specifically, we first introduce an improved cost function to measure the categorical and numerical attributes uniformly and assign the weight to each attribute. We also propose two entropy metrics to calculate weight values and embed them into the k-prototypes algorithm for mixed data clustering in smart grid. Finally, we compare our proposed algorithm with existing clustering algorithms and the experimental results show that our algorithm is effective for anomaly detection in smart grid.

Online information diffusion generates huge volumes of social activities (eg. tweets, retweets posts, comments, likes) among individuals. Existing information diffusion modeling techniques are oblivious to conformity of individuals during the diffusion process, a fundamental human trait according to social psychology theories. Intuitively, conformity captures the extent to which an individual complies with social norms or expectations. In this paper, we present a novel framework called chassis to characterize online information diffusion by bridging classical information diffusion model with conformity from social psychology. To this end, we first extend "Hawkes Process", a well-known statistical technique utilized to model information diffusion, to quantitatively capture two flavors of conformity, informational conformity and normative conformity, hidden in activity sequences. Next, we present a novel semi-parametric inference approach to learn the proposed model. Experimental study with real-world datasets demonstrates the superiority of chassis to state-of-the-art conformity-unaware information diffusion models.

The Internet of Vehicles (IoV) has been considered as the most promising architecture for the next generation of Intelligent Transportation System (ITS). However, due to its decentralized feature, the security problem is always the bottleneck problem in IoV. In this paper we consider the secure data exchange protocol in decentralized Vehicle to Vehicle (V2V) Network with physical layer security approach. With skillfully designing of precoding at transmitter, only the desired OBU can receive valid data, meanwhile the attackers can only receive a superposition signal which cannot recover the original data. After N N 1 time slots, each OBU can recover the data from all other OBUs. Performance analysis has been conducted for the proposed protocol in view of theory and simulation. The transmission efficiency analysis result shows that the time slots cost of proposed protocol is less than the naive data exchange. Besides, the security analysis demonstrate that the proposed protocol can successfully prevent instantaneous attack as well as continuous attack.

Due to the low latency and high speed of 5G networks, the Internet of Vehicles (IoV) under the 5G network has been rapidly developed and has broad application prospects. The Third Generation Partnership Project (3GPP) committee has taken remote diagnosis as one of the development cores of IoV. However, how to ensure the security of remote diagnosis and maintenance services is also a key point to ensure vehicle safety, which is directly related to the safety of vehicle passengers. In this paper, we propose a secure and efficient authentication scheme based on extended chebyshev chaotic maps for remote diagnosis and maintenance in IoVs. In the proposed scheme, to provide strong security, anyone, such as the vehicle owner or the employee of the Vehicle Service Centre (VSC), must enter the valid biometrics and password in order to enjoy or provide remote diagnosis and maintenance services, and the vehicle and the VSC should authenticate each other to ensure that they are legitimate. The security analysis and performance evaluation results show that the proposed scheme can provide robust security with ideal efficiency.

Summary Mobile social network has become a necessary part in our daily life, and location‐based services (LBSs) provide unprecedented convenience to mobile users. However, these attracting services are accompanied with privacy disclosures, including location privacy and query privacy. Mobile users have to expose their personal information to untrusted location‐based service provider (LSP) in order to obtain relevant service data. To address these privacy issues, we proposed a personalized range‐sensitive privacy‐preserving scheme, called PRPS, which considers the relationship between locations, query ranges, and query contents. Moreover, PRPS employs map storing algorithm (MSA) to facilitate the storage of two‐dimensional local map, reducing the cost of storage. Furthermore, range estimating algorithm (REA) adopts binary quad‐tree to decide the query radius of each submitted location, avoiding inference attacks by adversary. The requirements generating algorithm (RGA) selects relevant query content for each dummy location, guaranteeing mobile user's location privacy and query privacy. Finally, we illustrate the privacy analysis to proof PRPS's privacy degree; then, the performance and privacy evaluation results indicate that the proposed PRPS is effective and efficient.

As a development of the next generation of mobile communication networks and systems (5G), the Third-Generation Partnership Project (3GPP) committee has standardized a new 5G authentication and key-agreement (5G-AKA) protocol to ensure the access security of a mobile equipment. However, there are still some security vulnerabilities in the 5G-AKA protocol, and there is no authentication protocol proposed for massive device concurrent connection by the 3GPP working groups. In this article, we propose a novel lightweight and secure access authentication scheme named lightweight secure access authentication (LSAA) that contains two lightweight extended Chebyshev chaotic maps-based access authentication protocols for two types of 3GPP standard mobile devices: 1) common user equipment (UE) and 2) massive machine-type communication (mMTC) devices. Our proposed protocols can achieve several security functionalities, including mutual authentication, session-key establishment, identity privacy protection, and perfect forward/backward secrecy (PFS/PBS). In addition, the proposed protocols are lightweight in nature compared with the 5G-AKA. In order to comprehensively and accurately evaluate LSAA, we carry out formal security analysis by employing two formal verification tools Proverif and Scyther, and informal security analysis on the proposed protocols. We further evaluate the performance of the proposed protocols with regard to authentication signaling cost, authentication communication cost, authentication computational cost, and authentication storage cost. The security evaluation and performance analysis results show that our proposed protocols can provide advanced security and high efficiency.

Space information network (SIN), which has the characteristics of large capacity, high reliability, and wide coverage, can be effectively applied to the Internet of Things (IoT) business. However, SIN is vulnerable to various attacks due to its highly exposed links, and the power and processing capacity of satellites and IoT devices (IoTDs) are usually limited. Meanwhile, massive IoTDs connecting to SIN in an instant incur a severe signaling congestion and there is no authentication protocol proposed for massive IoTDs in SIN. In this article, we first propose a novel semi-aggregated signature mechanism and session key agreement mechanism. Then, based on the above two mechanisms, we propose a novel access authentication scheme named lattice-based access authentication (LAA) which contains two types of lattice-based authentication protocols: 1) LAA for massive IoTDs and 2) LAA for a single IoTD. The security analysis by employing the formal provable security analysis, the automation verification tool named Scyther, the Burrows-Abadi-Needham-logic, and the informal security analysis demonstrate that our proposed protocols successfully achieve these security properties, including mutual authentication, conditional anonymity, unlinkability, data confidentiality, data integrity, unforgeability, undeniability, key establishment, perfect forward/backward secrecy (PFS/PBS), and resistance against protocol attacks and quantum attacks. We further evaluate the performance of our proposed protocols with regard to signaling overhead, transmission overhead, computational overhead, and authentication delay, which shows that our proposed protocols can provide high efficiency.

Deep neural networks have achieved excellent performance in computer vision and many other fields. However, this good result is at the cost of high model complexity and expensive hardware requirement. Hence, it is an important topic to keep the performance of the deep model with as few parameters as possible. Inspired by the knowledge distillation (KD) method, a hyperspectral imagery (HSI) classification method based on compressed neural network is proposed in this letter. Different from the original KD method, virtual samples are adopted to describe the classification boundary of the teacher network more accurately, which effectively improves the classification accuracy of the student network. Experimental results of three real HSI data sets show that the superiority of the proposed method to the state-of-the-art algorithms in terms of classification accuracy and model complexity.

Face authentication is vulnerable to media-based virtual face forgery (MVFF) where adversaries display photos/videos or 3D virtual face models of victims to spoof face authentication systems. In this paper, we propose a liveness detection mechanism, called FaceCloseup, to protect the face authentication on mobile devices. FaceCloseup detects MVFF-based attacks by analyzing the distortion of face regions in a user's closeup facial videos captured by built-in camera on mobile device. It can detect MVFF-based attacks with an accuracy of 99.48%.

The vehicular reputation management scheme based on blockchain has been studied with the rapidly development of blockchain technology. Existing works mainly focus on provisioning the security of the reputation management scheme by using the blockchain technology. However, the broadcast latency of blocks and the throughput capacity of the blockchain also have significant impacts on the performance of the reputation management scheme. This paper proposes a hierarchical blockchain based reputation management scheme in vehicular networks. Our hierarchical blockchain architecture assures that each vehicle can obtain the latest reputation value of any neighboring vehicle in a short time. Moreover, our designed blockchain structure aims at reducing the confirmation time of reputation values and increasing the capacity of blockchain. We finally conduct simulation experiments to compare our proposed scheme with the existing schemes.

Server and Network Assisted Dynamic adaptive streaming over HTTP (SAND) is a promising technology to cope with the dramatic increase in video streaming traffic. The new emerging Mobile Edge Computing (MEC) paradigm may further facilitate bitrate adaptation and video transcoding in a SAND system with the help of local edge servers. A critical issue in MEC-SAND framework is to guarantee Quality of Experience (QoE) for clients while achieving efficient utilization of edging network resources. In this paper, we aim to develop an adaptive video delivery scheme to minimize the delay in MEC assisted ultra-dense networks. In our scheme, each client is mapped to a server that better fits its requirements and transmission condition, and a bitrate selection mechanism is exploited to decide the best video version for the client. Besides, time-consuming transcoding tasks are carefully scheduled considering edge computing capacity. We formulate a Mix-Integer Non-Linear Programming (MINLP) problem to jointly determine cell association, bitrate adaption, and computing resource allocation. We exploit the generalized Benders decomposition method to reduce the solving complexity of the formulated problem. Numerical results validate the effectiveness and efficiency of the proposed scheme.

Mobile crowdsensing is recognized to be a promising paradigm wherein location-based sensing tasks are outsourced to participants carrying mobile devices. A prominent issue of crowdsensing is to guarantee the sensing coverage by appropriately recruiting participating devices, which requires the disclosure of participants' locations and leads to potential location privacy threats. In this paper, we aim to develop a privacy-preserving participant recruiting scheme for mobile crowdsensing, which guarantees the crowdsensing coverage while preserving participants' location differential privacy against a semi-honest crowdsensing aggregator. Briefly, based on the differential private geo-indistinguishability method, we enable candidate participants to locally perturb their location data. With the obfuscated location information, we formulate the crowdsensing coverage optimization as an Integer Program (IP), and develop a 1-(1 - 1/f)f-approximation algorithm, which yields a near-optimal participant recruiting solution. Through extensive simulations, we demonstrate the tradeoff between privacy preservation and crowdsensing utility, and show that satisfactory crowdsensing coverage can be achieved while preserving the participants' differential location privacy.

The physical layer security of OFDM communication systems is getting more and more attention. Protecting the channel from eavesdroppers has received some attention. Artificial noise is generally considered to utilize the null space of the intended receiver's channel to interfere with the eavesdropper's channel, but it may not be able to defend against passive eavesdroppers. In this paper, a novel anti-eavesdropping OFDM system is proposed by using artificial pilot noise. Pilots play an important role in demodulating OFDM signal. In our proposed scheme, an auxiliary device called Helper would generate noise signals that overlap on the pilots sent by the user terminal. With the knowledge of the signals sent by the Helper, the receiver can decode the user terminal signal but eavesdroppers cannot. This would prevent eavesdroppers from overhearing the information transmitted between the user terminal and the receiver, so as to provide a secure communication for the user terminal. Finally, we use wireless open access research platform (WARP) v3 to test the feasibility and security of the scheme in practice. Our experimental results show that the proposed scheme can provide a secure communication in the environments with passive eavesdroppers.

In the field of network traffic analysis, Deep Packet Inspection (DPI) technology is widely used at present. However, the increase in network traffic has brought tremendous processing pressure on the DPI. Consequently, detection speed has become the bottleneck of the entire application. In order to speed up the traffic detection of DPI, a lot of research works have been applied to improve signature matching algorithms, which is the most influential factor in DPI performance. In this paper, we present a novel method from a different angle called Precisely Guided Signature Matching (PGSM). Instead of matching packets with signature directly, we use supervised learning to automate the rules of specific protocol in PGSM. By testing the performance of a packet in the rules, the target packet could be decided when and which signatures should be matched with. Thus, the PGSM method reduces the number of aimless matches which are useless and numerous. After proposing PGSM, we build a framework called PGSM-DPI to verify the effectiveness of guidance rules. The PGSM-DPI framework consists of PGSM method and open source DPI library. The framework is running on a distributed platform with better throughput and computational performance. Finally, the experimental results demonstrate that our PGSM-DPI can reduce 59.23% original DPI time and increase 21.31% throughput. Besides, all source codes and experimental results can be accessed on our GitHub.

As the number of people using social networks increases, users are often assigned to different domains for better digital right management. However, they are no longer satisfied with accessing data just in one domain with the rapid expansion of information, so there is an urgent need for a way to access data among different domains while guarantee their privacy and security. In this paper, we propose a cross-domain access control scheme based on multi-authority attribute-based encryption for big data from social networks. In our construction, we use a hybrid encryption algorithm that based on symmetric encryption and CP-ABE not only to achieve cross-domain access control, but also to improve the efficiency of the system. Besides, the trusted proxy users for proper storage of symmetric keys ensures the security of the system. Meanwhile, the use of an inter-domain trusted proxy user, instead of an intra-domain data owner to formulate an access control policy, reduces the huge computational overhead associated with the ciphertext re-encryption technology and greatly improves the efficiency of the entire system. Finally, security analysis and performance analysis show that the proposed scheme is a secure and trusted networking.

Speaker verification has played a significant role in authentication with the booming development of smartphones and intelligent terminals in recent years. However, most speaker verification systems directly store the users original voiceprint template data (or called acoustic features). In this paper, we reveal the insecurity and sensitiveness of voiceprint template data by carrying out spoofing attacks on speaker verification systems using genetic algorithm. Meanwhile, multiple generation models based on different genetic algorithms (standard genetic algorithm, multiple population genetic algorithm) are proposed, but also the effects of these generation models are compared. Moreover, experimental results on state-of-the-art text-independent speaker verification techniques (such as i-vector, GMM-UBM) clearly demonstrate that our generated attack voice with leaked voiceprint template data can completely imitate users and pass the speaker verification.

Photo sharing on Online Social Networks (OSNs) has become one of the most popular social activities in our daily life. However, some associated friends or bystanders in the photos may not want to be viewed due to privacy concerns. In this paper, we propose the design, implementation and evaluation of HideMe, a framework to preserve the associated users’ privacy for online photo sharing. HideMe acts as a plugin to existing photo sharing OSNs, and it enables the following: a) extraction of factors when users upload their photos, b) associated friends in the uploaded photos are able to set their own privacy policies based on scenarios, instead of a photo-by-photo setting, c) any user in other friend’s uploaded photos could be hidden away from unwanted viewers based on one time policy generation. We also design a distance-based algorithm to identify and protect the privacy of bystanders. Moreover, HideMe not only protects users’ privacy but also reduces the system overhead by a carefully designed face matching algorithm. We have implemented a prototype of HideMe, and evaluation results have demonstrated its effectiveness and efficiency.

The Narrow Band Internet of Things (NB-IoT) system has become an important branch of the Internet of Everything and is an indispensable part in future fifth Generation (5G) network. However, there is currently no effective access authentication scheme for the NB-IoT system in the future 5G network. According to the current 3GPP standard, NB-IoT devices still use the traditional access authentication method to perform the mutual authentication with the network, which may bring a lot of signaling and communication overheads. This problem will be more prominent when sea of NB-IoT devices simultaneously are activated in the 5G network. In this paper, we propose a quantum-resistance access authentication and data distribution scheme for massive NB-IoT devices. This scheme can implement access authentication and data transmission for a group of NB-IoT devices at the same time based on the lattice-based homomorphic encryption technology. Our scheme can not only greatly reduce the network burden, but also can achieve the strong security including privacy protection and resisting quantum attacks. Performance analysis results show that our solution has the desired efficiency.

The narrowband Internet of Things (NB-IoT) system has become an integral part of the future fifth generation (5G) network. Although the NB-IoT system has gradually been improved in the traditional LTE network currently, the NB-IoT system does not have an effective access authentication scheme in the future 5G network. According to the current 3rd Generation Partnership Project (3GPP) standard, NB-IoT devices still use the traditional access authentication method to perform mutual authentication with the network, which may bring a large amount of signaling and communication overhead. This problem will be magnified in a large-scale device environment in the future 5G network. In this article, a quantum resistance access authentication and data distribution scheme is proposed for large-scale NB-IoT devices. The scheme can simultaneously implement access authentication and data transmission of a group of NB-IoT devices based on the lattice-based homomorphic encryption technology. Our scheme not only greatly reduce the network burden but also achieve strong security, including privacy protection and anti-quantum attacks. The performance analysis results show that our scheme has the ideal efficiency.

Internet of Things (IoT) has been developed rapidly to make our life easier. In many IoT applications (e.g., smart homes, healthcare, etc.), all the IoT devices should be synchronized in time. However, some malicious nodes located in the IoT network can influence the time synchronization, which may interrupt the IoT system and lead to serious accidents. Therefore, it is critical and challenging to guarantee the accuracy and consistency of time during the time synchronization among all the IoT devices. In this paper, we propose a blockchain-based scheme to assure the security during time synchronization in IoT. Specifically, a publicly verifiable ledger is utilized to record and broadcast time, which can minimize many attacks from external environments. The use of multiple time sources can avoid the vulnerabilities caused by the centralized generation of accurate time. Moreover, the decentralized structure of this scheme has the advantage of adapting the changes of network topology. By employing an improved practical Byzantine fault tolerance consensus mechanism, time synchronization can be implemented efficiently. At last, the analysis results show that our proposed scheme can achieve the desired security with high efficiency.

With the proliferation of the Internet of Things (IoT), wireless sensor networks, mobile social networks, crowdsensing applications, and beyond, huge amounts of data are being explosively generated every day. While big data and IoT bring well-understood benefits to increase knowledge and improve the quality of our daily lives, they also face wide attacking surfaces, and thus raise critical concerns on notions of trust, security, and privacy. One of the fundamental questions is how to collect, store, and process the huge amounts of data in a secure and privacy-preserving manner, without compromising the potential of capitalization on big data and IoT.

We consider the problem of protecting individual user's location privacy at the trace-level and study the privacy-utility trade-off, which has key applications in privacy-preserving location-based service. Existing works on Location Privacy Protection Mechanisms (LPPMs) have mainly focused on protecting single location, without taking into account the temporal correlations among locations within the trace, which can lead to higher privacy leakage when considering the whole trace. However, to date, there lacks a formal framework to quantify the trace-level location privacy leakage, and a practical mechanism to release location traces in an optimal and online manner. In this paper, we endeavor to solve this problem using an information-theoretic approach. We first propose a location trace privacy metric based on the mutual information between the original and released trace in an offline setting, and formulate the optimal location trace release problem that minimizes trace-level privacy leakage given a utility constraint. We also propose a privacy metric to capture trace-level privacy leakage in an online setting. As directly computing these metrics incur exponential complexity w.r.t. the trace length, we obtain upper and lower bounds on the trace-level privacy leakage by exploiting the Markov structure of the temporal location correlations, which are efficiently computable. The proposed upper bounds enable us to derive efficient online solutions (i.e., LPPMs) by modifying Blahut-Arimoto algorithm in rate-distortion theory. Then we validate the proposed upper and lower bounds and the actual leakage of our LPPM through extensive experiments over both synthetic and real-world location data sets. Our results show the superiority of our LPPM over existing LPPMs in terms of trace-level privacy-utility tradeoff, which is more conspicuous when the location trace is more correlated.

Fog computing, as an extension of cloud computing, outsources the encrypted sensitive data to multiple fog nodes on the edge of Internet of Things (IoT) to decrease latency and network congestion. However, the existing ciphertext retrieval schemes rarely focus on the fog computing environment and most of them still impose high computational and storage overhead on resource-limited end users. In this paper, we first present a Lightweight Fine-Grained ciphertexts Search (LFGS) system in fog computing by extending Ciphertext-Policy Attribute-Based Encryption (CP-ABE) and Searchable Encryption (SE) technologies, which can achieve fine-grained access control and keyword search simultaneously. The LFGS can shift partial computational and storage overhead from end users to chosen fog nodes. Furthermore, the basic LFGS system is improved to support conjunctive keyword search and attribute update to avoid returning irrelevant search results and illegal accesses. The formal security analysis shows that the LFGS system can resist Chosen-Keyword Attack (CKA) and Chosen-Plaintext Attack (CPA), and the simulation using a real-world dataset demonstrates that the LFGS system is efficient and feasible in practice.

Cellular Vehicle-to-Everything (C-V2X) as a wireless communication technology has been widely investigated to guarantee road safety and support autonomous driving. Nevertheless, due to high-mobility of nodes and time-varying topology of networks, C-V2X networks always face many security threats. We identify a new Denial of Service (DoS) attack in which a small number of nodes maliciously reserve the communication resources in a C-V2X network by requesting high priority V2X services frequently. A malicious vehicle detection scheme is then developed to defend against this attack. Different from the detection methods based on traffic analysis, the Mobile Edge Computing (MEC) server only needs to focus on detecting nodes with high malicious probability in our scheme. Thus, we propose a model to estimate the probability that a node is malicious based on its communication resources consumption state. Simulation results demonstrate not only the feasibility of our attack but also desirable performance of the proposed malicious node detection mechanism.

With the development of virtualization technology and fast expansion of network-scale, SDN has been employed in various cases from campus networks to cloud data center networks. However, SDN networks are also facing some new security issues, relative to the traditional networks. In this work, we demonstrate a novel network isolation attack in SDN networks, called Network Harvesting, that lets an attacker can access to the user's network privileges without the awareness of victim and OpenFlow SDN architecture, which significantly increases persistence. We then present a defense, SpoofDefender, that prevents network isolation attacks or other spoofing attacks by leveraging SDN's data and control plane separation, global network view, and programmatic control of the network, while building upon IEEE 802.1x and encryption. In addition, we also implement SpoofDefender on ONOS 1.10.4 and Mininet with a real network, and extensive simulation results demonstrate that our proposed SpoofDefender is highly effective in terms of computation and communication costs.

Online medical primary diagnosis system, which can provide the pre-diagnosis service anywhere anytime, has attracted considerable interest. However, the flourish of online medical primary diagnosis system still faces many serious challenges since the sensitivity of personal health information and service provider''s diagnosis model. In this paper, we propose an efficient and privacy-preserving medical primary diagnosis scheme based on k-nearest-neighbors classification (kNN), called EPDK. With EPDK, medical users can ensure that their sensitive health information are not compromised during the online medical diagnosis process, and service provider can provide high-accuracy service without revealing its diagnosis model. Specifically, based on lightweight multiparty random masking and polynomial aggregation techniques, a medical user preprocesses her/his query vector before sending out and the preprocessed vector is directly operated in the service provider without obtaining original data, meanwhile, the primary diagnosis result cannot be achieved by anyone except the medical user. Through extensive analysis, we show that EPDK can resist multifarious known security threats, and has significantly lower computation complexity than existing schemes. Moreover, performance evaluations via implementing EPDK in the real environment demonstrate that EPDK is highly efficient in terms of computation overhead.

Now, the authors’ life is full of vast amount of information, the era of information has arrived. So the content‐centric networks face severe challenges in dealing with a huge range of content requests, bringing protection and sharing concerns of the content. How to protect information in the network efficiently and securely for the upcoming 5G era has become a problem. The authors propose a scheme based on a blockchain to solve the privacy issues in content‐centric mobile networks for 5G. The authors implement the mutual trust between content providers and users. Besides, the openness and tamper‐resistant of the blockchain ledger ensure the access control and privacy of the provider. With the help of a miner, selected from users, the authors can maintain the public ledger expediently. Also, in return, the authors share the interesting data with low overhead, network delay and congestion, and then achieve green communication.

Cloud-based mobile crowd-sourcing has been an attractive solution to provide data storage and share services for resource-limited mobile devices in a privacy-preserving manner, but how to enable mobile users to issue search queries and achieve fine-grained access control over ciphertexts simultaneously is still a big challenge for various circumstances. Although the ciphertext-policy attribute-based keyword search technology combining attribute-based encryption with searchable encryption has become a hot research topic, it just deals with equivalent attributes rather than more practical attribute comparisons, like “greater than” or “less than.” In this paper, we devise a practical cryptographic primitive called attribute-based multi-keyword search scheme to support comparable attributes through utilizing 0-encoding and 1-encoding. Formal security analysis proves that our scheme is selectively secure against chosen-keyword attack in generic bilinear group model and extensive experiments using real-world dataset demonstrate that our scheme can drastically decrease both computational and storage costs.

With the pervasiveness of location-aware mobile terminals and the popularity of social applications, location-based social networking service (LBSNS) has brought great convenience to people's life. Meanwhile, proximity detection, which makes LBSNS more flexible, has aroused widespread concern. However, the prosperity of LBSNS still faces many severe challenges on account of users' location privacy and data security. In this paper, we propose two efficient and privacy-preserving proximity detection schemes, named arbitrary geometric range query for polygons (AGRQ-P) and arbitrary geometric range query for circles (AGRQ-C), for location-based social applications. With proposed schemes, a user can choose any area on the map, and query whether her/his friends are within the region without divulging the query information to both social application servers and other users, meanwhile, the accurate locations of her/his friends are also confidential for the servers and the query user. Specifically, with algorithms based on ciphertext of geometric range query, users' query and location information is blurred into ciphertext in client, thus no one but the user knows her/his own sensitive information. Detailed security analysis shows that various security threats can be defended. In addition, the proposed schemes are implemented in an IM APP with a real LBS dataset, and extensive simulation results over smart phones further demonstrate that AGRQ-P and AGRQ-C are highly efficient and can be implemented effectively.

Machine Type Communication (MTC), as one of the most important wireless communication technologies in the future wireless communication, has become the new business growth point of mobile communication network. It is a key point to achieve seamless handovers within Evolved-Universal Terrestrial Radio Access Network (E-UTRAN) for massive MTC (mMTC) devices in order to support mobility in the Long Term Evolution-Advanced (LTE-A) networks. When mMTC devices simultaneously roam from a base station to a new base station, the current handover mechanisms suggested by the Third-Generation Partnership Project (3GPP) require several handover signaling interactions, which could cause the signaling load over the access network and the core network. Besides, several distinct handover procedures are proposed for different mobility scenarios, which will increase the system complexity. In this paper, we propose a simple and secure uniform group-based handover authentication scheme for mMTC devices based on the multisignature and aggregate message authentication code (AMAC) techniques, which is to fit in with all of the mobility scenarios in the LTE-A networks. Compared with the current 3GPP standards, our scheme can achieve a simple authentication process with robust security protection including privacy preservation and thus avoid signaling congestion. The correctness of the proposed group handover authentication protocol is formally proved in the Canetti-Krawczyk (CK) model and verified based on the AVISPA and SPAN.

The rapid development of communication and network technologies including mobile networks and GPS presents new characteristics of OSNs. These new characteristics pose extra requirements on the access control schemes of OSNs, which cannot be satisfied by relationship-based access control currently. In this paper, we propose a hybrid access control model (HAC) which leverages attributes and relationships to control access to resources. A new policy specification language is developed to define policies considering the relationships and attributes of users. A path checking algorithm is proposed to figure out whether paths between two users can fit in with the hybrid policy. We develop a prototype system and demonstrate the feasibility of the proposed model.

Traditional medical privacy data are at a serious risk of disclosure, and many related cases have occurred over the years. For example, personal medical privacy data can be easily leaked to insurance companies, which not only compromises the privacy of individuals, but also hinders the healthy development of the medical industry. With the continuous improvement of cloud computing and big data technologies, the Internet of Things technology has been rapidly developed. Radio frequency identification (RFID) is one of the core technologies of the Internet of Things. The application of the RFID system to the medical system can effectively solve this problem of medical privacy. RFID tags in the system can collect useful information and conduct data exchange and processing with a back-end server through the reader. The whole process of information interaction is mainly in the form of ciphertext. In the context of the Internet of Things, the paper presents a lightweight RFID medical privacy protection scheme. The scheme ensures security privacy of the collected data via secure authentication. The security analysis and evaluation of the scheme indicate that the protocol can effectively prevent the risk of medical privacy data being easily leaked.

More and more sensitive information of big data are being now stored in the cloud for it offers quality service and convenient management. But the Cloud Server cannot be fully trusted because it may leak information which raises security and privacy concerns of the stored data. Therefore, sensitive data has to be encrypted before shifted to the cloud. It is a great convenience for authorized users to directly retrieve the encrypted data stored in the cloud by keyword search. As we all known, returning all the matching documents is a waste of network bandwidth. Although there are some schemes that support sorting fuzzy multi-keyword search, and they are generally based on edit distance to looking for fuzzy alternatives set, which leads to significantly larger index file size and higher search complexity. Then we rank the search results according to the matching score between keywords and documents. In this paper, we give a novel multi-keyword fuzzy and sortable search scheme. We use n-gram technology to realize fuzzy search and give a novel ranking search scheme based on the fuzzy multi- keyword search. For the matching files may be more than one, there are differences between the original query keywords and the fuzzy keywords. When ranking files, we will not only consider the relevance score between the index keywords and the documents, but also the similarity between the query and keywords. Thus we calculate the comprehensive matching score of the file to the set of query keywords, which is more efficient and accurate.

With the prevalence of Location-Based Service (LBS), the concern of location privacy has raised the attention of users in recent years. Although most smartphone users carefully set the location-related permissions of Apps, they ignore the fact that Apps can obtain the location of devices through accessing WiFi list. Therefore, it is necessary to investigate the severe consequence of WiFi list leakage. Taking TrackU, an efficient scheme on Android for example, this paper proves that it is possible to obtain users location data by monitoring the WiFi list, without directly relying on the location-related permissions. At first, the WiFi APs (Access Points) is scanned periodically. Meanwhile, the location provided by LBS providers (e.g, Google or Baidu LBS SDK) is queried. Then a drift adjusting algorithm is proposed to obtain exact locations considering a set of factors, such as the historical location information, the average signal strengths and the changing of WiFi list. Based on the obtained data, we design an activity detection algorithm to infer users daily activities exactly. Finally, we implement the TrackU and carry out a series of experiments with 40 volunteers from 8 cities in China. The experiment results show that our design can detect 91.6% of users activities by monitoring the WiFi list.

The handover mechanism with the assist of mobile relay mounted in high-speed trains has been researched to support continuous communication services for Long-Term Evolution Advanced (LTE-A) high-speed rail networks. According to the third Generation Partnership Project (3GPP) standard, the handover process for Mobile Relay Nodes (MRNs) from a donor eNB (DeNB) to another is the same as that for the common User Equipment (UE), which requires several rounds of message exchange with a complex key management mechanism. In addition, it cannot achieve the mutual authentication in handover procedures. In this paper, we propose a handover authentication mechanism based on trajectory prediction for mobile relays. In our scheme, the mutual authentication and key agreement between a MRN and the target DeNB is accomplished with ideal efficiency. Compared with the current 3GPP standards and other related schemes, our scheme effectively reduces the handover delays and at the same time provides strong security protection. Security analysis by using the formal verification tool AVISPA and SPAN and performance evaluation results show the security and efficiency of our scheme.

Mobile Social Networks (MSNs) provide a variety of social networking applications in mobile environment, where social group finds and recruits potential members easily. Unfortunately, users enjoy these conveniences at the cost of revealing their personal data. Additionally, people usually ignore a critical factor, Impact Factor (IF), which is used to quantify group members' influence on their groups, since a group member with larger IF generally has a greater influence on potential new member recommendation. In this paper, we propose IF-RG, an IF-based group recommendation scheme with privacy preservation in MSNs. First, we construct a transmission matrix and exploit PageRank algorithm to compute and update group members' IFs. The average variation of IF is formed to measure convergence speed of the iteration method of computing IF. To make sure that the larger IFs, the more influence, IF, Ochiai similarity function and weighted majority rule are integrated in the novel matching degree between stranger and group. The fuzzy matrix algorithm not only protects users' privacy, but also helps our scheme to support group recommendation when not every one in the groups is online. Finally, security and performance are analyzed and evaluated via detailed simulations.

With the pervasiveness of mobile devices and the development of biometric technology, biometric identification, which can achieve individual authentication relies on personal biological or behavioral characteristics, has attracted widely considerable interest. However, privacy issues of biometric data bring out increasing concerns due to the highly sensitivity of biometric data. Aiming at this challenge, in this paper, we present a novel privacy-preserving online fingerprint authentication scheme, named e-Finga, over encrypted outsourced data. In the proposed e-Finga scheme, the user's fingerprint registered in trust authority can be outsourced to different servers with user's authorization, and secure, accurate and efficient authentication service can be provided without the leakage of fingerprint information. Specifically, an improved homomorphic encryption technology is introduced to achieve an efficient online fingerprint matching algorithm over encrypted FingerCode data in the outsourcing scenarios. Through detailed analysis, we show that e-Finga can resist various security threats and serve efficient and accurate online fingerprint authentication.

Since the dummy-based method can provide precise query results without any requirement for a third party or key sharing, it has been widely used to protect the user's location privacy in location-based services. However, the neighboring location sets submitted in consecutive requests always include a close spatiotemporal correlation, which enables the adversary to identify some dummies. Therefore, the existing dummy-based schemes cannot protect the user's location privacy completely. To solve this problem, based on the dummies generated by the existing schemes, this paper filters out the dummies that can be identified by taking into account of the spatiotemporal correlation from three aspects, namely time reachability, direction similarity and in-degree/out-degree. In this way, the rest dummies can satisfy the user's personalized privacy protection requirement. Security analysis shows that the proposed scheme successfully perturbs the spatiotemporal correlation between neighboring location sets, therefore, it is infeasible for the adversary to distinguish the user's real location from the dummies. Furthermore, extensive experiments indicate that the proposal is able to protect the user's location privacy effectively and efficiently.

How to control the access of the huge amount of big data becomes a very challenging issue, especially when big data are stored in the cloud. Ciphertext-policy attribute-based encryption (CP-ABE) is a promising encryption technique that enables end-users to encrypt their data under the access policies defined over some attributes of data consumers and only allows data consumers whose attributes satisfy the access policies to decrypt the data. In CP-ABE, the access policy is attached to the ciphertext in plaintext form, which may also leak some private information about end-users. Existing methods only partially hide the attribute values in the access policies, while the attribute names are still unprotected. In this paper, we propose an efficient and fine-grained big data access control scheme with privacy-preserving policy. Specifically, we hide the whole attribute (rather than only its values) in the access policies. To assist data decryption, we also design a novel attribute bloom filter to evaluate whether an attribute is in the access policy and locate the exact position in the access policy if it is in the access policy. Security analysis and performance evaluation show that our scheme can preserve the privacy from any linear secret-sharing schemes access policy without employing much overhead.

With the pervasiveness of mobile devices and the development of wireless communication technique, location-based services (LBSs) have made our life more convenient, and the polygons spatial query, which can provide more flexible LBS, has attracted considerable interest recently. However, the flourish of polygons spatial query still faces many challenges including the query information privacy. In this paper, we present an efficient and privacy-preserving polygons spatial query framework for location-based services, called Polaris. With Polaris, the LBS provider (LP) outsources the encrypted LBS data to cloud server (CS), and the registered user can query any polygon range to get accurate LBS results without divulging his/her query information to the LP and CS. Specifically, an efficient special polygons spatial query (SPSQ) algorithm over ciphertext is constructed, based on an improved homomorphic encryption technology over composite order group. With SPSQ, Polaris can search outsourced encrypted LBS data in CS by the encrypted request, and respond the encrypted polygons spatial query results accurately. Detailed security analysis shows that the proposed Polaris can resist various known security threats. In addition, performance evaluations via implementing Polaris on smartphone and workstation with real LBS dataset demonstrate Polaris' effectiveness in term of real environment.

As one of the most important techniques in IoT, NFC (Near Field Communication) is more interesting than ever. NFC is a short-range, high-frequency communication technology well suited for electronic tickets, micropayment, and access control function, which is widely used in the financial industry, traffic transport, road ban control, and other fields. However, NFC is becoming increasingly popular in the relevant field, but its secure problems, such as man-in-the-middle-attack and brute force attack, have hindered its further development. To address the security problems and specific application scenarios, we propose a NFC mobile electronic ticket secure payment and verification scheme in the paper. The proposed scheme uses a CS E-Ticket and offline session key generation and distribution technology to prevent major attacks and increase the security of NFC. As a result, the proposed scheme can not only be a good alternative to mobile e-ticket system but also be used in many NFC fields. Furthermore, compared with other existing schemes, the proposed scheme provides a higher security.

Several factors (e.g., balancedness, good correlation immunity) are considered as important properties of Boolean functions for using in cryptographic primitives. A Boolean function is perfect algebraic immune if it is with perfect immunity against algebraic and fast algebraic attacks. There is an increasing interest in construction of Boolean function that is perfect algebraic immune combined with other characteristics, like resiliency. A resilient function is a balanced correlation-immune function. This paper uses bivariate representation of Boolean function and theory of finite field to construct a generalized and new class of Boolean functions on even variables by extending the Carlet-Feng functions. We show that the functions generated by this construction support cryptographic properties of 1-resiliency and (sub)optimal algebraic immunity and further propose the sufficient condition of achieving optimal algebraic immunity. Compared experimentally with Carlet-Feng functions and the functions constructed by the method of first-order concatenation existing in the literature on even (from 6 to 16) variables, these functions have better immunity against fast algebraic attacks. Implementation results also show that they are almost perfect algebraic immune functions.

With the advances of machine learning algorithms and the pervasiveness of network terminals, the online medical prediagnosis system, which can provide the diagnosis of healthcare provider anywhere anytime, has attracted considerable interest recently. However, the flourish of online medical prediagnosis system still faces many challenges including information security and privacy preservation. In this paper, we propose an e fficient and privacy-preserving online medical prediagnosis framework, called eDiag, by using nonlinear kernel support vector machine (SVM). With eDiag, the sensitive personal health information can be processed without privacy disclosure during online prediagnosis service. Specifically, based on an improved expression for the nonlinear SVM, an efficient and privacy-preserving classification scheme is introduced with lightweight multiparty random masking and polynomial aggregation techniques. The encrypted user query is directly operated at the service provider without decryption, and the diagnosis result can only be decrypted by user. Through extensive analysis, we show that eDiag can ensure that users' health information and healthcare provider's prediction model are kept confidential, and has significantly less computation and communication overhead than existing schemes. In addition, performance evaluations via implementing eDiag on smartphone and computer demonstrate eDiag's effectiveness in term of real online environment.

In this paper, we propose a secure incentive scheme to achieve fair and reliable cooperative (SIRC) downloading in highway vehicular ad hoc networks (VANETs). SIRC can stimulate vehicle users to help download-and-forward packets for each other and consists of cooperative downloading and forwarding phase. During the cooperative downloading phase, SIRC utilizes “virtual checks” associated with the designated verifier signature to ensure fair and secure cooperation. Meanwhile, to minimize the payment risk of the client vehicle, partial prepayment strategy is adopted, i.e., the vehicles involved in downloading packets can only obtain part of the check before the client vehicle confirms the packet reception. During the cooperative forwarding phase, a profit-sharing model associated with an aggregating Camenisch-Lysyanskaya (CL) signature can stimulate cooperation and reduce the authentication overhead. In addition, we develop a reputation system to encourage cooperation and punish malicious vehicles. The aggregating CL signature and the symmetric cryptosystem are applied to resist various attacks, including injection/removing attack, free riding attack, submission refusal attack, and denial of service attacks. Extensive simulation results are given to show that the proposed SIRC can achieve a high download success rate and low average download delay with moderate cryptographic computation and communication overhead.

Uploading data streams to a resource-rich cloud server for inner product evaluation, an essential building block in many popular stream applications (e.g., statistical monitoring), is appealing to many companies and individuals. On the other hand, verifying the result of the remote computation plays a crucial role in addressing the issue of trust. Since the outsourced data collection likely comes from multiple data sources, it is desired for the system to be able to pinpoint the originator of errors by allotting each data source a unique secret key, which requires the inner product verification to be performed under any two parties' different keys. However, the present solutions either depend on a single key assumption or powerful yet practically-inefficient fully homomorphic cryptosystems. In this paper, we focus on the more challenging multi-key scenario where data streams are uploaded by multiple data sources with distinct keys. We first present a novel homomorphic verifiable tag technique to publicly verify the outsourced inner product computation on the dynamic data streams, and then extend it to support the verification of matrix product computation. We prove the security of our scheme in the random oracle model. Moreover, the experimental result also shows the practicability of our design.

Researchers and engineers have paid more attention to cloud-based application systems, whose features are virtualization and services provisioning. Fortunately the technology can be just right to healthcare. Meanwhile, security has also become more challenging. Although many cloud-based RFID authentication protocols have been proposed, some of them only improve the function and performance without considering security and privacy, and most of them are heavyweight. It is not appropriate in the field of healthcare, because improving the trustworthiness of anonymous virtual computing services should be the primary consideration. So we propose a lightweight privacy protection authentication scheme which can be applied in the cloud environment, in the scheme, service providers could be anonymous or unknown to the application consumer. Assuming many hospitals build a cloud platform together, the information of patient and his physician will be stored anonymously in the cloud. Patient can go to a doctor in any one hospital with a unique RFID tag. Reader may be fixed or mobile; Readers read tags and upload collected data to the cloud for further processing in real time. Compared with some traditional schemes, our scheme is lightweight, cost-efficient, elastic scalability, real-time, and easy to against synchronization attack.

Privacy issues in group matching problem have become one of the most important things in Mobile Social Networks (MSNs) currently. Mobile users may feel uncomfortable when releasing personal information to some irrelevant people or groups. In this paper, we propose a practical group matching scheme without employing any Trusted Third Party (TTP) for privacy-aware users in MSNs. We first propose a fuzzy matrix algorithm to generate user's authority instead of complex cryptographic computations to reduce the communication and computation overhead, and thus build a public set to store all the group members' profiles and authorities. As a result, our group matching does not need all the group members are online anymore. Moreover, we utilize the Ochiai similarity considering both of the number of common attributes and the size of each user's profile. The privacy and performance are analyzed and evaluated via detailed simulations.

Privacy issues in Location-Based Services (LBSs) have gained tremendous attentions in literature over recent years. Existing approaches always fail to provide dual privacy protection on both user's location and point of interest (POI), incur endurable system overhead, and produce high quality of services, simultaneously. To address these problems, we propose a time obfuscation-based scheme, termed TOP-privacy, which carefully generates and sends some dummy queries at leisure time to confuse adversaries with some background information. TOP-privacy employs a dummy query generation algorithm, which includes a dummy location selection module and a classified POI pool construction module. It first selects some location candidates with similar location distribution with the real user's, and then determines the optimal POI to construct the dummy query based on the similarity to the user's real POI. Security analysis and evaluation results indicate its effectiveness and efficiency.

As one of the core techniques in 5G, the Internet of Things is more interested than ever. Furthermore, radio frequency identification RFID plays a crucial role in Internet of Things development. Although the low-cost RFID system has wide prospect, it has to face with huge challenges because of potential security risks, privacy problems, and efficiency because of its restrictions on processing, storage, and power in RFID tags. One of the possible solutions in secure authentication of the low-cost RFID system is the lightweight RFID authentication protocol. A lightweight RFID mutual authentication protocol with cache in the reader is proposed in this paper, named LRMAPC. The LRMAPC can greatly reduce the computational and transmission cost. Especially, it can reduce computational costs greatly when a large number of tags want to be authenticated. We prove the correctness of LRMAPC using GNY logic. Compared with some existing works, LRMAPC achieves higher efficiency and stronger security. Furthermore, we developed LRMAPC into ULRMAPC, an ultralightweight RFID mutual authentication protocol with cache in the reader. Compared with SASI and Gossamer protocols, ULRMAPC also achieves higher efficiency and stronger security in storage and computation cost. Copyright © 2015 John Wiley & Sons, Ltd.

Abstract As a public key cryptographic primitive, attribute‐based encryption (ABE) is promising in implementing fine‐grained access control in cloud computing. However, before ABE comes into practical applications, two challenging issues have to be addressed, that is, users' attribute privacy protection and access policy update. In this paper, we tackle the aforementioned challenge for the first time by formalizing the notion of anonymous ciphertext‐policy attribute‐based proxy re‐encryption (anonymous CP‐ABPRE) and giving out a concrete construction. We propose a novel technique called match‐then‐re‐encrypt, in which a matching phase is additionally introduced before the re‐encryption phase. This technique uses special components of the proxy re‐encryption key and ciphertext to anonymously check whether the proxy can fulfill a proxy re‐encryption or not. Theoretical analysis and simulation results demonstrate that our anonymous CP‐ABPRE scheme is secure and efficient. Copyright © 2016 John Wiley & Sons, Ltd.

In order to realize attribute-based data sharing in cloud computing, multi-authority attribute-based encryption (MA-ABE) is extremely attractive. However, most of the existing MA-ABE schemes cannot support a fully large attribute universe and are not suitable for resource-constrained mobile data owners in that the computation cost in secret key generation and encryption is extremely heavy. To tackle the earlier challenges, we propose an online/offline MA-ABE scheme, which realizes both the online/offline secret key generation and the online/offline encryption while supporting a fully large attribute universe. In the offline phase, one global-identity authority and multiple attribute authorities do the majority of the work to issue attribute secret keys before knowing users' global identity and attributes. The data owner can perform most of the encryption computation tasks before knowing the actual message and access structure. Furthermore, the online phase can rapidly assemble the final decryption key and ciphertexts when related specifications become known. Particularly, global-identity authority and attribute authorities need not to cooperate in the whole process. Our online/offline MA-ABE scheme allows the access policies encoded in linear secret sharing schemes. The formal selective security proof and extensive performance analysis indicate that our scheme is very suitable for data sharing in mobile cloud computing. Copyright © 2016 John Wiley & Sons, Ltd.

Abstract Support vector machine (SVM) classification, which can handle large data sets in high dimensional spaces, has been widely applied in numerous settings nowadays, such as genetic match, spam detection, and financial prediction. However, because of the data's sensitivity and classifiers' confidentiality, how to provide a privacy‐preserving SVM classification has attracted considerable interest recently. Aiming at these privacy challenges, in this paper, we present an efficient and privacy‐preserving classification service query framework, named EPCS, for the linear kernel SVM classifier. Specifically, based on lightweight multiparty random masking and polynomial aggregation techniques, EPCS preserves the privacy of users' data and SVM classifier efficiently during the process of user query. Through detailed security analysis, we show that EPCS can resist various security threats. In addition, we implement EPCS in Java and evaluate EPCS over a smart phone and a PC, and extensive simulation results demonstrate that the EPCS framework can indeed achieve high efficiency and effectiveness in SVM classification. Copyright © 2016 John Wiley & Sons, Ltd.

Search over encrypted data is a critically important enabling technique in cloud computing, where encryption-before-outsourcing is a fundamental solution to protecting user data privacy in the untrusted cloud server environment. Many secure search schemes have been focusing on the single-contributor scenario, where the outsourced dataset or the secure searchable index of the dataset are encrypted and managed by a single owner, typically based on symmetric cryptography. In this paper, we focus on a different yet more challenging scenario where the outsourced dataset can be contributed from multiple owners and are searchable by multiple users, i.e., multi-user multi-contributor case. Inspired by attribute-based encryption (ABE), we present the first attribute-based keyword search scheme with efficient user revocation (ABKS-UR) that enables scalable fine-grained (i.e., file-level) search authorization. Our scheme allows multiple owners to encrypt and outsource their data to the cloud server independently. Users can generate their own search capabilities without relying on an always online trusted authority. Fine-grained search authorization is also implemented by the owner-enforced access policy on the index of each file. Further, by incorporating proxy re-encryption and lazy re-encryption techniques, we are able to delegate heavy system update workload during user revocation to the resourceful semi-trusted cloud server. We formalize the security definition and prove the proposed ABKS-UR scheme selectively secure against chosen-keyword attack. To build confidence of data user in the proposed secure search system, we also design a search result verification scheme. Finally, performance evaluation shows the efficiency of our scheme.

Abstract With a great variety of potential applications, machine‐type communications (MTC) is gaining a tremendous interest from mobile network operators and research groups. MTC is standardized by the 3rd Generation Partnership Project (3GPP), which has been regarded as the promising solution facilitating machine‐to‐machine communications. In the latest standard, 3GPP proposes a novel architecture for MTC, in which the MTC server is located outside the operator domain. However, the connection between the 3GPP core network and MTC server in this scenario is insecure; consequently, there are distrustful relationships among MTC device, core network, and MTC server. If the security issue is not well addressed, all applications involved in MTC cannot be put into the market. To address this problem, we propose an end‐to‐end security scheme for MTC based on the proxy‐signature technique, called E2SEC. Specifically, both the MTC device and MTC server can establish strong trustful relationships with each other by using the proxy signatures issued by the 3GPP core network. Moreover, we present some implementation considerations of E2SEC and analyze the performance during authentication by comparing the operational cost of three cases that apply three different signature algorithms, that is, ElGamal, Schnorr, and DSA. Through security analysis by using Automatic Cryptographic Protocol Verifier (ProVerif), we conclude that the proposed E2SEC scheme can achieve the security goals and prevent various security threats. Copyright © 2015 John Wiley & Sons, Ltd.

With the rapid development of the cloud computing, personal health record (PHR) has attracted great attention of many researchers all over the world recently. However, PHR, which is often outsourced to be stored at a third party, has many security and efficiency issues. Therefore, the study of secure and efficient Personal Health Record Scheme to protect users' privacy in PHR files is of great significance. In this paper, we present a secure and efficient Personal Health Record scheme called SE-PHR. In the SE-PHR scheme, we divide the users into personal domain (PSD) and public domain (PUD) logically. In the PSD, the Key-Aggregate Encryption called KAE is exploited. For the users of PUD, we use outsource-able multi-authority attribute-based encryption (MA-ABE) to largely eliminate the overhead for users and support efficient attribute revocation without updating the user's private key. Our scheme also presents a new algorithm which enables dynamic modification of access policies. Function and performance testing results show the security and efficiency of the proposed SE-PHR.

Super-peer p2p networks inherit the advantages of P2P networks, such as pooling together the shared data (documents in our system) across peers, self- organizing, and fault-tolerance. Most of the searches in p2p networks are plaintext-based and do not satisfy the pressing demands for high security in massive information sharing applications. In this paper, a super-peer p2p system is designed that supports ciphertext-based search for relevant documents. In the proposed system, a super-peer which contains all the encrypted index of documents in all the peers subordinate to it is added to the p2p system, and the indexing efficiency is improved by utilizing the indexing structure that binary trees are nested in a B+ tree. Finally, the experimental results demonstrate the proposed scheme can achieve acceptable security and efficiency.

Machine Type Communication (MTC) has quickly become the driving force of the mobile operators for a large number of real-time network applications. The design of the MTC security mechanisms in the mobile environments of the Long Term Evaluation-Advanced (LTE-A) networks is the major point of the future research for the LTE-A security. When a good deal of MTC devices simultaneously move to a new eNodeB (eNB), the current third Generation Partnership Project (3GPP) handover mechanisms require several handover signaling interactions, which could not only cause the signaling load over the access network and the core network, but also increase the energy consumption of MTC devices. In this paper, we enhance the current handover mechanisms and propose an efficient group-based anonymity handover authentication protocol for a lot of MTC devices with mobility, which is to fit in with all of the mobility scenarios in the LTE-A networks. Compared with the current 3GPP standards, our scheme can not only largely reduce the signaling costs in both the access network and the core network, but also achieve the privacy preservation.

Machine Type Communication (MTC) as one of the most important wireless communication technologies in the future wireless communication, has become the new business growth point of mobile communication network. It is a key point to achieve seamless handovers within Evolved Universal Terrestrial Radio Access Network (E-UTRAN) for a large number of MTC devices in order to support mobility in the Long Term Evolution Advanced (LTE-A) networks. When a good deal of MTC devices simultaneously roam from a base station to another, the current handover mechanisms suggested by the third Generation Partnership Project (3GPP) require several handover signaling interactions, which could cause the signaling load over the network nodes. Besides, several distinct handover procedures are proposed for different mobility scenarios, which will increase the system complexity. In this paper, we propose a simple and secure uniform group-based handover authentication scheme for a lot of MTC devices based on the multi-signature and aggregate message authentication codes (AMAC) techniques, which is to fit in with all of the mobility scenarios in the LTE-A networks. Compared with the current 3GPP standards, our scheme can achieve a simple authentication process with robust security protection, and thus avoid signaling congestion.

With the pervasiveness of smartphones and the advance of wireless body sensor networks (WBSNs), mobile healthcare (m-healthcare) has attracted considerable interest recently. In m-Healthcare, users' smartphones serve as bridges connecting their WBSNs and the healthcare center (HCC), i.e., send users' personal health information (PHI) collected by WBSNs to the HCC and receive the feedback. However, users' smartphones are not always available (e.g., left at home or out of power), resulting in an unexpected interruption of medical services sometimes, which are not considered in most existing schemes for m-healthcare. In this paper, we propose a lightweight and privacy-preserving agent data transmission scheme for m-healthcare in opportunistic social networks on condition that the smartphone is not available. By using the proposed protocol, we can provide uninterrupted healthcare while keeping the user's identity and PHI private during the agent transmitting of PHI. Security and performance analysis show that the proposed scheme can realize privacy-preservation and achieve secure end-to-end communication for m-healthcare, and is suitable for resource-limited WBSNs.

The popularity of Location-Based Services (LBSs) have resulted in serious privacy concerns recently. Mobile users may lose their privacy while enjoying kinds of social activities due to the untrusted LBS servers. Many Privacy Protection Mechanisms (PPMs) are proposed in literature by employing different strategies, which come at the cost of either system overhead, or service quality, or both of them. In this paper, we design privacy-preserving strategies for both of the users and adversaries in service quality aware LBSs. Different from existing approaches, we first define and point out the importance of the Fine-Grained Side Information (FGSI) over existing concept of the side information, and propose a Dual-Privacy Metric (DPM) and Service Quality Metric (SQM). Then, we build analytical frameworks that provide privacy-preserving strategies for mobile users and the adversaries to achieve their goals, respectively. Finally, the evaluation results show the effectiveness of our proposed frameworks and the strategies.

Searchable encryption is a promising technique enabling meaningful search operations to be performed on encrypted databases while protecting user privacy from untrusted third-party service providers. However, while most of the existing works focus on common SQL queries, geometric queries on encrypted spatial data have not been well studied. Especially, circular range search is an important type of geometric query on spatial data which has wide applications, such as proximity testing in Location-Based Services and Delaunay triangulation in computational geometry. In this poster, we propose two novel symmetric-key searchable encryption schemes supporting circular range search. Informally, both of our schemes can correctly verify whether a point is inside a circle on encrypted spatial data without revealing data privacy or query privacy to a semi-honest cloud server. We formally define the security of our proposed schemes, prove that they are secure under Selective Chosen-Plaintext Attacks, and evaluate their performance through experiments in a real-world cloud platform (Amazon EC2). To the best of our knowledge, this work represents the first study in secure circular range search on encrypted spatial data.

Privacy protection is critical for Location-Based Services (LBSs). In most previous solutions, users query service data from the untrusted LBS server when needed, and discard the data immediately after use. However, the data can be cached and reused to answer future queries. This prevents some queries from being sent to the LBS server and thus improves privacy. Although a few previous works recognize the usefulness of caching for better privacy, they use caching in a pretty straightforward way, and do not show the quantitative relation between caching and privacy. In this paper, we propose a caching-based solution to protect location privacy in LBSs, and rigorously explore how much caching can be used to improve privacy. Specifically, we propose an entropy-based privacy metric which for the first time incorporates the effect of caching on privacy. Then we design two novel caching-aware dummy selection algorithms which enhance location privacy through maximizing both the privacy of the current query and the dummies' contribution to cache. Evaluations show that our algorithms provide much better privacy than previous caching-oblivious and caching-aware solutions.

Encrypted data search allows cloud to offer fundamental information retrieval service to its users in a privacy-preserving way. In most existing schemes, search result is returned by a semi-trusted server and usually considered authentic. However, in practice, the server may malfunction or even be malicious itself. Therefore, users need a result verification mechanism to detect the potential misbehavior in this computation outsourcing model and rebuild their confidence in the whole search process. On the other hand, cloud typically hosts large outsourced data of users in its storage. The verification cost should be efficient enough for practical use, i.e., it only depends on the corresponding search operation, regardless of the file collection size. In this paper, we are among the first to investigate the efficient search result verification problem and propose an encrypted data search scheme that enables users to conduct secure conjunctive keyword search, update the outsourced file collection and verify the authenticity of the search result efficiently. The proposed verification mechanism is efficient and flexible, which can be either delegated to a public trusted authority (TA) or be executed privately by data users. We formally prove the universally composable (UC) security of our scheme. Experimental result shows its practical efficiency even with a large dataset.

Social networking service (SNS) is one of the major technological applications based on Web 2.0, which can help users to express their views and share information with others. How to describe the information diffusion process in online SNS accurately has attracted considerable interest recently. However, almost all existing models focus on a single online SNS tool and face many challenges with multiple online SNS tools. In this paper, we turn to users’ privacy setting policies and propose a general stochastic model with multiple diffusion mechanisms in online SNS, called DMPS. Specifically, we first define a privacy protection mechanism based on information sharing in online SNS and classify nodes according to different privacy setting policies; then, we define the states of the nodes and information dissemination rules with dynamics of infectious disease; finally, we describe the evolution process of different nodes by dynamic evolution equations. Detailed simulations and analysis show that the DMPS can precisely describe the diffusion process with multiple diffusion mechanisms and have the same characteristics as a diffusion process in real online SNS. As a result, DMPS can be used to identify the underlying diffusion mechanism of information and forecast its trend in online SNS.

Abstract Privacy‐preserving aggregation for smart grid communications, which precisely meets the requirement of periodically collecting users' electricity consumption while preserving privacy of each individual user, has been extensively studied in recent years. However, most of existing privacy‐preserving aggregation schemes are only focused on the summation aggregation. In this paper, based on the lattice cryptographic technique, we propose a novel privacy‐preserving dual‐functional aggregation scheme (PDA) for smart grid communications. With our proposed PDA scheme, each individual user just reports one data, then multiple statistic values, that is, mean and variance, of all users can be computed by the data & control center in the smart grid, while the privacy of each individual user can still be protected. Detailed security analyses demonstrate that our proposed PDA scheme is secure and robust. In addition, extensive performance evaluations also show that our proposed PDA scheme is efficient in terms of computational and communication overheads when the number of considered users is within an acceptable range. Copyright © 2015 John Wiley & Sons, Ltd.

Machine Type Communication (MTC), as one of the most important communication approaches in the future mobile communication, has drawn more and more attention. To meet the requirements of low power consumption of devices and mass device transmission is the key issue to achieve MTC applications security in the Long Term Evolution (LTE) networks. When a large number of MTC devices simultaneously connect to the network, each MTC device needs to implement an independent access authentication procedure in the current third Generation Partnership Project (3GPP) standard, which will cause a severe signaling congestion in the LTE network. In this paper, we propose a group-based access authentication scheme, by which a good deal of MTC devices can be simultaneously authenticated by the network and establish an independent session key with the network respectively. Our scheme cannot only greatly reduce the signal transmission for mass of devices to the network and thus avoid the signaling overload over the LTE network, but also achieve robust security including Key Forward/Backward Secrecy (KFS/KBS) and non-repudiation verification. The experimental results and formal verification by using the TLA+ and TLC show that the proposed scheme is secure against various malicious attacks.

Quite recently, Cheng and Jiang[1]designed an efficient homomorphic message authentication code (MAC) scheme for authentication in network coding, and claimed that the newly proposed scheme can achieve much higher security level with a formal security proof. However, in this comment, we will point out there exists a design flaw in their proposed scheme, leading to an inherent vulnerability, and also show an inaccuracy in their formal security proof. We hope that by identifying the design flaw, similar mistakes can be avoided in future design of homomorphic MAC authentication for network coding.

Bundle authentication, which ensures the authenticity and integrity of bundles, is critical in space Delay/disruption-Tolerant Networks (DTNs). When bundle fragment services are needed, the previous solutions directly using digital signatures suffer from heavy computational costs and bandwidth overheads. This paper addresses the issue of fragment authentication for Bundle Protocol by exploiting erasure codes and the batch transmission characteristic of DTNs. Erasure codes are adopted to allow all the fragments of a bundle to equally share only one signature, to tolerate high delays as well as unexpected loss of connectivity. Following this generic idea, we present two approaches, both of which are effective in filtering inauthentic fragments as early as possible. The first one takes a surprisingly low bandwidth overhead, while it makes all received fragments of a bundle to be removed when there is an inauthentic one, because of its failure in locating the inauthentic fragments. Considering this defect, we present an improved scheme which is able to detect inauthentic fragments thanks to a special hash chain and then only remove these inauthentic ones. The performance simulation demonstrates that both our schemes significantly reduce bandwidth overheads and computational costs as compared to the prior works.

With data storage and sharing services in the cloud, users can easily modify and share data as a group. To ensure shared data integrity can be verified publicly, users in the group need to compute signatures on all the blocks in shared data. Different blocks in shared data are generally signed by different users due to data modifications performed by different users. For security reasons, once a user is revoked from the group, the blocks which were previously signed by this revoked user must be re-signed by an existing user. The straightforward method, which allows an existing user to download the corresponding part of shared data and re-sign it during user revocation, is inefficient due to the large size of shared data in the cloud. In this paper, we propose a novel public auditing mechanism for the integrity of shared data with efficient user revocation in mind. By utilizing the idea of proxy re-signatures, we allow the cloud to re-sign blocks on behalf of existing users during user revocation, so that existing users do not need to download and re-sign blocks by themselves. In addition, a public verifier is always able to audit the integrity of shared data without retrieving the entire data from the cloud, even if some part of shared data has been re-signed by the cloud. Moreover, our mechanism is able to support batch auditing by verifying multiple auditing tasks simultaneously. Experimental results show that our mechanism can significantly improve the efficiency of user revocation.

Recently, a public auditing protocol for shared data called Panda (IEEE Transactions on Services Computing, doi: 10.1109/TSC.2013.2295611) was proposed to ensure the correctness of the outsourced data. A distinctive feature of Panda is the support of data sharing and user revocation. Unfortunately, in this letter, we show that Panda is insecure in the sense that a cloud server can hide data loss without being detected. Specifically, we show that even some stored file blocks have been lost, the server is able to generate a valid proof by replacing a pair of lost data block and its signature with another block and signature pair. We also provide a solution to the problem while preserving all the desirable features of the original protocol.

As one of the next frontiers in wireless communications, Machine Type Communications not only support traditional short message service, but also provide multimedia services, such as voice and video services. The 3rd Generation Partnership Project has made a series of technical specifications of Multimedia Broadcast/Multicast Service for cellular network. However, they are inflexible for group key update, and will cause the core network over-burdened. Hence It is not suitable for Machine Type Communications, especially when mass MTC devices enjoy a specific Multimedia Broadcast/Multicast Service. A group key agreement in multimedia service for MTC communications is proposed to overcome the above disadvantages. The scheme could transmit group key information using broadcast messages and reduce the interacting rounds. Analysis of the scheme indicates that it is of backward and forward security and collusion resistance. In addition, the proposal could lower the communication costs to staying constant compared with that of MBMS.

Cloud computing promises users massive scale outsourced data storage services with much lower costs than traditional methods. However, privacy concerns compel sensitive data to be stored on the cloud server in an encrypted form. This posts a great challenge for effectively utilizing cloud data, such as executing common SQL queries. A variety of searchable encryption techniques have been proposed to solve this issue; yet efficiency and scalability are still the two main obstacles for their adoptions in real-world datasets, which are multi-dimensional in general. In this paper, we propose a tree-based public-key Multi-Dimensional Range Searchable Encryption (MDRSE) to overcome the above limitations. Specifically, we first formally define the leakage function and security of a tree-based MDRSE. Then, by leveraging an existing predicate encryption in a novel way, our tree-based MDRSE efficiently indexes and searches over encrypted cloud data with multi-dimensional tree structures (i.e., R-trees). Moreover, our scheme is able to protect single-dimensional privacy while previous efficient solutions fail to achieve. Our scheme is selectively secure, and through extensive experimental evaluation on a large-scale real-world dataset, we show the efficiency and scalability of our scheme.

With extensive promising applications, machine to machine (M2M) communications or machine-type communication (MTC) have attached a tremendous interest among mobile network operators and research groups. Supporting multiple MTC devices has been considered as an essential requirement in M2M communications. How to achieve a secure and efficient access authentication for a group of MTC devices during roaming is a challenging issue. In this paper, in order to simultaneously resolve the access security and efficiency in MTC, we propose a secure and efficient group roaming scheme for MTC between 3GPP and WiMAX networks, named SEGR, which is characterized by authenticating all MTC devices in a group simultaneously and speeding up the process of authentication through adopting a novel certificateless aggregate signature technique. Through security analysis, the proposed SEGR can provide robust security, especially overcome the drawback of key escrow in identity-based (ID-based) aggregate signature schemes. In addition, performance evaluations in terms of communication overhead and computation complexity demonstrate that SEGR is more efficient than those traditional schemes.

Location-Based Services (LBSs) have been one of the most popular activities in our daily life. Users can send queries to the LBS server easily to learn their surroundings. However, these location-related queries may result in serious privacy concerns since the un-trusted LBS server has all the information about users and may track them in various ways. In this paper, we propose two dummy-based solutions to achieve k-anonymity for privacy-area aware users in LBSs with considering that side information may be exploited by adversaries. We first choose some candidates based on a virtual circle or grid method, then blur these candidates into the final positions of dummy locations based on the entropy-based privacy metric. Security analysis and evaluation results indicate that the V-circle solution can significantly improve the privacy anonymity level. The V-grid solution can further enlarge the cloaking region while keeping similar privacy level.

Location-based services (LBSs) are attracting more and more attentions with the increasing popularity of mobile devices and online social networking. In LBSs, users can conveniently obtain their interested information by sending queries to the LBS server. However, users' queries include their identities, locations, interests, etc, which may result in the leakage of users' trajectory and other sensitive information. Although the existing obfuscation and location anonymization techniques along with a long-term pseudonym can protect users' location privacy to some extent, users' real identities and moving trajectories can still be deduced through long-term observation and side information aided inference attacks. To address these problems, we propose a dynamic pseudo-ID system, where unlinkable pseudo-IDs are used by users to completely hide their identities in the queries, in order to break the link between users' identities and their locations. Moreover, we employ certificates to ensure the verifiability and traceability of the dynamic pseudo-IDs. Security analysis and evaluation results show that the proposed scheme can enhance user' privacy and is feasible to implement into mobile devices.

Proximity-based mobile social networks (PMSNs) are becoming increasingly popular in recent years with the explosive growth of mobile devices, where a user can find a best matching friend in vicinity through profile matching. However, the matching process calls for the publication of users' personal information, which conflicts with users' growing privacy concerns about revealing their profiles to strangers. To achieve privacy-preserving friend discovery, many schemes are proposed based on traditional cryptographic methods, such as homomorphic and commutative encryption, which inevitably introduce tremendous overhead to the system and are not practical for the resource-limited mobile devices. In this paper, we propose an Efficient Weight-based Private Matching (EWPM) protocol, which provides a realistic matching approach considering both the number of common interests and the corresponding weights on them. In EWPM, we utilize the Confusion Matrix Transformation (CMT) algorithm to achieve a secure and efficient matching. Security analysis and detailed simulations show that our proposed scheme can realize privacy-preserving friend discovery with higher efficiency.

In Location-Based Services (LBSs) mobile users submit location-related queries to the untrusted LBS server to get service. However, such queries increasingly induce privacy concerns from mobile users. To address this problem, we propose FGcloak, a novel fine-grained spatial cloaking scheme for privacy-aware mobile users in LBSs. Based on a novel use of modified Hilbert Curve in a particular area, our scheme effectively guarantees k-anonymity and at the same time provides larger cloaking region. It also uses a parameter σ for users to make fine-grained control on the system overhead based on the resource constraints of mobile devices. Security analysis and empirical evaluation results verify the effectiveness and efficiency of our scheme.

Location-Based Service (LBS) has become a vital part of our daily life. While enjoying the convenience provided by LBS, users may lose privacy since the untrusted LBS server has all the information about users in LBS and it may track them in various ways or release their personal data to third parties. To address the privacy issue, we propose a Dummy-Location Selection (DLS) algorithm to achieve k-anonymity for users in LBS. Different from existing approaches, the DLS algorithm carefully selects dummy locations considering that side information may be exploited by adversaries. We first choose these dummy locations based on the entropy metric, and then propose an enhanced-DLS algorithm, to make sure that the selected dummy locations are spread as far as possible. Evaluation results show that the proposed DLS algorithm can significantly improve the privacy level in terms of entropy. The enhanced-DLS algorithm can enlarge the cloaking region while keeping similar privacy level as the DLS algorithm.

Search over encrypted data is a critically important enabling technique in cloud computing, where encryption-before-outsourcing is a fundamental solution to protecting user data privacy in the untrusted cloud server environment. Many secure search schemes have been focusing on the single-contributor scenario, where the outsourced dataset or the secure searchable index of the dataset are encrypted and managed by a single owner, typically based on symmetric cryptography. In this paper, we focus on a different yet more challenging scenario where the outsourced dataset can be contributed from multiple owners and are searchable by multiple users, i.e. multi-user multi-contributor case. Inspired by attribute-based encryption (ABE), we present the first attribute-based keyword search scheme with efficient user revocation (ABKS-UR) that enables scalable fine-grained (i.e. file-level) search authorization. Our scheme allows multiple owners to encrypt and outsource their data to the cloud server independently. Users can generate their own search capabilities without relying on an always online trusted authority. Fine-grained search authorization is also implemented by the owner-enforced access policy on the index of each file. Further, by incorporating proxy re-encryption and lazy re-encryption techniques, we are able to delegate heavy system update workload during user revocation to the resourceful semi-trusted cloud server. We formalize the security definition and prove the proposed ABKS-UR scheme selectively secure against chosen-keyword attack. Finally, performance evaluation shows the efficiency of our scheme.

Location-Based Services (LBSs) have become one of the most popular activities and affected our daily life a lot. Mobile users can enjoy kinds of conveniences by submitting their location and interest-related queries to the LBS server. However, since these queries may expose sensitive information to the untrusted LBS server, privacy concerns arise. To address the serious privacy issues, we propose a novel collaborative scheme, EPcloak, which combines a privacy-preserving spatial cloaking algorithm and caching to protect user's privacy. Different from existing schemes, users in EPcloak cache their past service data for future use. When LBSs are needed, a user first uses a Local Searching Algorithm (LSA) to look for service data from the nearby friends of a collaborative group through Ad Hoc networks. If the requirements cannot be satisfied, the user uses a Spatial Cloaking Algorithm (SCA) to forward the query to another user at a certain distance away through a set of forwarders. That user will query the LBS server to get service data for a larger area that can cover the original user's query area, and send the data back to the original user. SCA protects both user's location privacy and query privacy. Evaluation results indicate that our proposed LSA and SCA are effective and efficient.

SUMMARY Radio frequency identification (RFID) has been regarded as one of the 10 important technologies in the 21st century. Because of its capability to rapidly and accurately collect and process data in real‐time, RFID has been widely applied in many areas, such as Internet of Things and Smart Grid. However, the existing security threats become more severe toward RFID authentication scheme. The traditional security mechanisms cannot be used in RFID directly because of the limitations of processing capability, storage space, and power supply of RFID tags. In this paper, we propose a revocable secure efficient lightweight RFID authentication scheme (RSEL). To achieve authentication efficiency, the key of the tag is chosen to reduce the number of hash computing in the database. Furthermore, the key is stored in the database and updated constantly with the tag to prevent the tracking and synchronization attacks. The valid period of each tag is stored in the database so that RSEL can revoke the expired tag. The correctness of RSEL has been proved using GNY logic. The performance of RSEL in terms of security and efficiency is evaluated. Compared with other existing approaches, RSEL achieves stronger security and higher efficiency. Copyright © 2013 John Wiley & Sons, Ltd.

SUMMARY The Extensible Authentication Protocol (EAP) framework aims to realize a flexible authentication for wireless networks. However, a full EAP authentication needs several round trips between a mobile node and the EAP server, and hence is unacceptable in a process of handoff authentication because of inefficient performance. Considering the advantage of the identity‐based cryptography, it is attractive to realize handoff authentication efficiently in the identity‐based setting. In this work, we propose a new identity‐based handoff authentication scheme in which a special double‐trapdoor chameleon hash function is used. Compared with the existing identity‐based handoff authentication construction, the main advantage of the proposed scheme eliminates the assumption that the private key generator is fully trusted. Besides, the detailed security analysis shows that the proposed scheme not only satisfies robust security properties but also enjoys desirable efficiency for the real‐world applications. Copyright © 2013 John Wiley & Sons, Ltd.

A full‐duplex wireless communications system is considered where K user wants to exchange common messages and private messages via a relay with multiple antennas simultaneously. The proposed scheme combines signal space alignment with zero forcing during multiple access channel phase and broadcast channel phase to cancel self‐interference. The authors can achieve high degree of freedom by using successive network coding and aligning multiple interfering signals. The result is extended to a more generalised Y channel circumstance where they can transfer each message with different interference‐free streams.

The roaming service enables mobile subscribers to access the internet service anytime and anywhere, which can fulfill the requirement of ubiquitous access for the emerging paradigm of networking, e.g., the Internet of Things (IoT). In this paper, we propose a conditional privacy-preserving authentication with access linkability (CPAL) for roaming service, to provide universal secure roaming service and multilevel privacy preservation. CPAL provides an anonymous user linking function by utilizing a novel group signature technique, which can not only efficiently hide users’ identities but also enables the authorized entities to link all the access information of the same user without knowing the user’s real identity. Specifically, by using the master linking key possessed by the trust linking server, the authorized foreign network operators or service providers can link the access information from the user to improve its service, while preserving user anonymity, e.g., using individual access information to analyze user preferences without revealing user’s identity. Furthermore, the subscribers can also use this functionality to anonymously query their usage of service. In addition, CPAL has an efficient revocation function, which revokes a group of users at the same time. Through extensive analysis, we demonstrate that CPAL resists various security threats and provides more flexible privacy preservation compared to the existing schemes. Meanwhile, performance evaluations demonstrate its efficiency in terms of communication and computation overhead.

ABSTRACT Consider a signed file that cannot be verified because of some fraction loss or noise, which might not affect the business of users. It would be desirable for the verifier to locate the damaged fractions so that the authenticity of other components of the file can be ensured. Thereafter, the verifier can decide whether or not to accept it. To address this issue, in this paper, we present a loss‐tolerant authentication scheme, which allows the verifier to locate the damaged elements in the signed file stream. Our idea is to allow the signed file to be fragmented, and each fragment is appended a short authentication tag, which enables the verifier to find the modified fragments in the signed file and verify the signature even if some fragments are lost. The proposed scheme adopts a combination of Reed–Solomon error correction and erasure codes, aiming to allow verifiers to locate the modified fractions and thereafter reconstruct the signature when sufficient original fractions of the file are obtained. Our scheme offers many potential applications such as loss‐tolerant transmission and storage. Copyright © 2013 John Wiley & Sons, Ltd.

ABSTRACT The low prices on cloud data storage and sharing services incentive users to outsource their data to the cloud. Because data stored in the cloud may be lost or corrupted, users are suggested to verify data integrity before the utilization of cloud data. A series of schemes have been proposed to enable a public verifier to efficiently check the correctness of cloud data without downloading the whole data from the cloud server. Unfortunately, few of them have considered about public verification onmulti‐ownercloud data while still preserving identity privacy of owners from public verifiers, where each block in these cloud data should be signed by multiple owners. In this paper, we design a novel public verification scheme to audit the integrity of multi‐owner data stored in the cloud. With our scheme, a public verifier is able to efficiently check the integrity of multi‐owner data with a very small communication cost compared with the size of the entire data. Meanwhile, the private identities of these owners are protected and not revealed to any public verifier. In addition, our scheme can also efficiently support group dynamics for multiple owners and enable batch verification. Security analyses and experimental results indicate our scheme is correct, secure and efficient. Copyright © 2013 John Wiley & Sons, Ltd.

ABSTRACT With the rapid development of cloud computing, more and more enterprises will outsource their sensitive data for sharing in a cloud. Many data sharing and access control schemes have been submitted. However, dynamic privileges among user groups were not considered. In many circumstances, some users may have higher privileges than others, and they can decrypt more contents than those with low privileges. Moreover, the data owner may want to dynamically control the privileges in data sharing. In this paper, we present an efficient framework for data sharing system to achieve dynamic privileges, basing on chameleon hash function and one‐way function. With this framework, any data sharing and access control scheme can be turned into a dynamic privileged scheme, in which the data owner can change the group of each user dynamically and change the structure of privileges flexibly when it is needed. The proposed framework requires much less storage than previous schemes in handling dynamic privileges among the users. Copyright © 2013 John Wiley & Sons, Ltd.

With cloud data services, it is commonplace for data to be not only stored in the cloud, but also shared across multiple users. Unfortunately, the integrity of cloud data is subject to skepticism due to the existence of hardware/software failures and human errors. Several mechanisms have been designed to allow both data owners and public verifiers to efficiently audit cloud data integrity without retrieving the entire data from the cloud server. However, public auditing on the integrity of shared data with these existing mechanisms will inevitably reveal confidential information—identity privacy—to public verifiers. In this paper, we propose a novel privacy-preserving mechanism that supports public auditing on shared data stored in the cloud. In particular, we exploit ring signatures to compute verification metadata needed to audit the correctness of shared data. With our mechanism, the identity of the signer on each block in shared data is kept private from public verifiers, who are able to efficiently verify shared data integrity without retrieving the entire file. In addition, our mechanism is able to perform multiple auditing tasks simultaneously instead of verifying them one by one. Our experimental results demonstrate the effectiveness and efficiency of our mechanism when auditing shared data integrity.

To ensure the authenticity, integrity, and confidentiality of bundles, the in-transit Protocol Data Units of bundle protocol (BP) in space delay/disruption tolerant networks (DTNs), the Consultative Committee for Space Data Systems bundle security protocol (BSP) specification suggests four IPsec style security headers to provide four aspects of security services. However, this specification leaves key management as an open problem. Aiming to address the key establishment issue for BP, in this paper, we utilize a time-evolving topology model and two-channel cryptography to design efficient and noninteractive key exchange protocol. A time-evolving model is used to formally model the periodic and predetermined behavior patterns of space DTNs, and therefore, a node can schedule when and to whom it should send its public key. Meanwhile, the application of two-channel cryptography enables DTN nodes to exchange their public keys or revocation status information, with authentication assurance and in a noninteractive manner. The proposed scheme helps to establish a secure context to support for BSP, tolerating high delays, and unexpected loss of connectivity of space DTNs.

With the growing popularity of cloud computing, huge amount of documents are outsourced to the cloud for reduced management cost and ease of access. Although encryption helps protecting user data confidentiality, it leaves the well-functioning yet practically-efficient secure search functions over encrypted data a challenging problem. In this paper, we present a verifiable privacy-preserving multi-keyword text search (MTS) scheme with similarity-based ranking to address this problem. To support multi-keyword search and search result ranking, we propose to build the search index based on term frequency- and the vector space model with cosine similarity measure to achieve higher search result accuracy. To improve the search efficiency, we propose a tree-based index structure and various adaptive methods for multi-dimensional (MD) algorithm so that the practical search efficiency is much better than that of linear search. To further enhance the search privacy, we propose two secure index schemes to meet the stringent privacy requirements under strong threat models, i.e., known ciphertext model and known background model. In addition, we devise a scheme upon the proposed index tree structure to enable authenticity check over the returned search results. Finally, we demonstrate the effectiveness and efficiency of the proposed schemes through extensive experimental evaluation.

With the increasing popularity of cloud computing, huge amount of documents are outsourced to the cloud for reduced management cost and ease of access. Although encryption helps protecting user data confidentiality, it leaves the well-functioning yet practically-efficient secure search functions over encrypted data a challenging problem. In this paper, we present a privacy-preserving multi-keyword text search (MTS) scheme with similarity-based ranking to address this problem. To support multi-keyword search and search result ranking, we propose to build the search index based on term frequency and the vector space model with cosine similarity measure to achieve higher search result accuracy. To improve the search efficiency, we propose a tree-based index structure and various adaption methods for multi-dimensional (MD) algorithm so that the practical search efficiency is much better than that of linear search. To further enhance the search privacy, we propose two secure index schemes to meet the stringent privacy requirements under strong threat models, i.e., known ciphertext model and known background model. Finally, we demonstrate the effectiveness and efficiency of the proposed schemes through extensive experimental evaluation.

Attribute-based encryption (ABE) has been widely studied recently to support fine-grained access control of shared data. Anonymous ABE, which is a relevant notion to ABE, further hides the receivers' attribute information in ciphertexts because many attributes are sensitive and related to the identity of eligible users. However, in existing anonymous ABE work, a user knows whether the attributes and the policy match or not only after repeating decryption attempts. And, the computation overhead of each decryption is high as the computational cost grows with the complexity of the access formula, which usually requires many pairings in most of the existing ABE schemes. As a result, this direct decryption method in anonymous ABE will suffer a severe efficiency drawback.

Supporting a massive number of machine-type communication (MTC) devices has been considered as an essential requirement in machine to machine (M2M) communications. Meanwhile, cyber security is of paramount importance in MTC; if MTC devices cannot securely access the networks through efficient authentication, all applications involving MTC cannot be widely accepted. One of research challenges in MTC is group authentication. A large number of MTC devices accessing the network simultaneously will cause a severe authentication signaling congestion. To solve this problem and reduce authentication overhead of the previous schemes based on public key cryptosystems, we propose a novel lightweight group authentication protocol for MTC in the long term evolution (LTE) networks based on aggregate message authentication codes (MACs), called LGTH, which can not only authenticate all MTC devices simultaneously, but also minimize the authentication overhead. Through security analysis, we conclude that the proposed LGTH can provide robust security, and avoid the authentication signaling congestion in the LTE networks. In addition, performance evaluations in terms of communication and computation overhead demonstrate that LGTH is more efficient than previous schemes.

Making new social interactions with other users in vicinity is a crucial service in Proximity-based Mobile Social Networks (PMSNs), where a user can find a best matching friend directly through the Bluetooth/WiFi interfaces built in her mobile device. In existing work for such services, users have to publish their interests to do the matching. However, it conflicts with users' growing privacy concerns about revealing their interests to strangers. To tackle this problem, we propose Weighted Average Similarity (WAS) algorithm, which considers both the number of common interests and the corresponding weights on them, to protect users' privacy without reliance on any Trusted Third Party (TTP). Users set their interests into several priority levels with different weights, then WAS can provide a high level similarity value among these participants without revealing any information about their common interests. The security and computation/communication overhead of our scheme are thoroughly analyzed and evaluated via detailed simulations.

Location-Based Services (LBSs) gain increasing popularity with the development of social networks and mobile devices. The mobile users enjoy convenience by submitting their private information. Nonetheless, the users' sensitive information may be abused by an un-trusted LBS server. Privacy concerned in LBSs can be categorized into two major types: location privacy and query privacy. In this paper, we propose a novel scheme, called Encounter-Based Privacy-Preserving Scheme (EPS), which allows a user to access an LBS server under the protection of k-anonymity on both her location privacy and query privacy. Without reliance on any Trusted Third Party (TTP), EPS uses a buffer on each user's mobile device to collect the queried information of the encountered users. To achieve k-anonymity, a user needs to choose k-1 records from her buffer, with the help of our location obfuscating algorithm and querying algorithm, the user's privacy can be protected. Evaluation results show the effectiveness and efficiency of our proposed EPS.

Location-Based Services (LBSs) are becoming increasingly popular in our daily life. In some scenarios, multiple users may seek data of same interest from a LBS server simultaneously or one by one, and they may need to provide their exact locations to the un-trusted LBS server in order to enjoy such a location-based service. Unfortunately, this will breach users' location privacy and security. To address this problem, we propose a novel collaborative system, MobiCache, which combines k-anonymity with caching together to protect user's location privacy while improving the cache hit ratio. Different from the traditional k-anonymity, our Dummy Selection Algorithm (DSA) chooses dummy locations which have not been queried before to increase the cache hit ratio. We also propose an enhanced-DSA to further improve the user's privacy as well as the cache hit ratio by assigning dummy locations which can make more contributions to cache hit ratio. Evaluation results show that the proposed DSA can increase the cache hit ratio and the enhanced-DSA can further improve the cache hit ratio as well as the user's privacy.

In the cloud, data is often shared by a group of users. To ensure the long-term correctness of cloud shared data, a third-party public verifier can be introduced to audit data integrity. During the auditing, protecting the privacy of the contributors of shared data from the public auditor is a fundamental issue. However, this makes it challenging to simultaneously support group membership dynamics efficiently, due to the significant amount of computation needed to update the signatures on shared data. In this paper, we propose a novel privacy-preserving public auditing mechanism for shared cloud data. With our proposed mechanism, a public verifier is able to audit the integrity of shared data without retrieving the entire data from the cloud, and also without learning private identity information of the group members. Group dynamics (user join and user revocation) are efficiently handled by outsourcing signature updating operations to the cloud via a secure proxy re-signature scheme. Experimental results show that our mechanism is highly efficient for dynamic groups.

Nowadays, many organizations outsource data storage to the cloud such that a member (owner) of an organization can easily share data with other members (users). Due to the existence of security concerns in the cloud, both owners and users are suggested to verify the integrity of cloud data with Provable Data Possession (PDP) before further utilization on data. However, previous methods either unnecessarily reveal the identity of a data owner to the untrusted cloud or any public verifiers, or introduce significant overheads on verification metadata to preserve anonymity. In this paper, we propose a simple and efficient publicly verifiable approach to ensure cloud data integrity without sacrificing the anonymity of data owners nor requiring significant verification metadata. Specifically, we introduce a security-mediator (SEM), which is able to generate verification metadata (i.e., signatures) on outsourced data for data owners. Our approach decouples the anonymity protection mechanism from the PDP. Thus, an organization can employ its own anonymous authentication mechanism, and the cloud is oblivious to that since it only deals with typical PDP-metadata, Consequently, there is no extra storage overhead when compared with existing non-anonymous PDP solutions. The distinctive features of our scheme also include data privacy, such that the SEM does not learn anything about the data to be uploaded to the cloud at all, which is able to minimize the requirement of trust on the SEM. In addition, we can also extend our scheme to work with the multi-SEM model, which can avoid the potential single point of failure existing in the single-SEM scenario. Security analyses prove our scheme is secure, and experiment results demonstrate our scheme is efficient.

With data services in the cloud, users can easily modify and share data as a group. To ensure data integrity can be audited publicly, users need to compute signatures on all the blocks in shared data. Different blocks are signed by different users due to data modifications performed by different users. For security reasons, once a user is revoked from the group, the blocks, which were previously signed by this revoked user must be re-signed by an existing user. The straightforward method, which allows an existing user to download the corresponding part of shared data and re-sign it during user revocation, is inefficient due to the large size of shared data in the cloud. In this paper, we propose a novel public auditing mechanism for the integrity of shared data with efficient user revocation in mind. By utilizing proxy re-signatures, we allow the cloud to re-sign blocks on behalf of existing users during user revocation, so that existing users do not need to download and re-sign blocks by themselves. In addition, a public verifier is always able to audit the integrity of shared data without retrieving the entire data from the cloud, even if some part of shared data has been re-signed by the cloud. Experimental results show that our mechanism can significantly improve the efficiency of user revocation.

With rapid developments of mobile devices and online social networks, users of Proximity-based Mobile Social Networks (PMSNs) can easily discover and make new social interactions with others at the cost of their growing privacy concerns. To address this problem, we propose a third party free scheme, P-match, to privately match the similarity with potential friends in vicinity. Unlike most existing work, P-match considers both the number of common interests and the corresponding priorities on each of them individually. The security and performance overhead of our scheme are then thoroughly analyzed and evaluated via detailed simulations.

Location-Based Services (LBSs) are becoming increasingly popular with rapid developments of social networks and location aware devices, such as smartphones and tablets. Users query the LBSs server and get service information about their surroundings. Unfortunately, these queries may lead to serious security and privacy concerns. It is very hard for users to access LBSs while keeping their privacy at the same time. To deal with this problem, we propose a novel scheme, called 3PLUS. It can significantly improve users' location privacy without reliance on the Trusted Third Party (TTP). Further more, it is simple to implement, and does not require changing the current structure of LBSs server. Users use a buffer to record the pseudo-locations, which come from both the history locations of herself and the encountered users. When two users encounter, by using their pseudonyms, they randomly choose a pseudo-location from their buffers and exchange with each other. Then she can find and submit k valid locations together to un-trusted LBSs server easily when the service is needed. Our evaluation results indicate us a hidden relationship between k, the buffer size S and exchanging number N.

Existing work on RFID authentication problems always make assumptions that 1) hash function can be fully used in designing RFID protocols; 2) channels between readers and the server are always secure. However, the first assumption is not suitable for EPC Class-1 Gen-2 tags, which has been challenged in many research work, while the second one cannot be adopted in mobile RFID applications where the wireless channels between readers and the server are always insecure. In this paper, we propose a new ultralightweight authentication protocol for mobile RFID systems. We only use bitwise XOR, and special constructed pseudo-random number generators (RNGs) to achieve our aims in insecure mobile RFID environment. Security analysis shows that our protocol can provide several privacy properties and avoid suffering from kinds of attacks, including tag anonymity, tag location privacy, reader privacy, forward secrecy, and mutual authentication, replay attack, desynchronization attack etc. We implement our protocol and compare authentication delays with several existing work, the results indicate us that our protocol significantly improves the efficiency.

Key-evolving cryptography is intended to mitigate the damage in case of a secret key compromise, one of the severest security threats to actual cryptographic schemes. In the public-key setting, the essential idea of key-evolving lies in updating the private key with time, while maintaining the same public key. Key evolution in encryption and signing has been well studied, especially in the identity-based (ID-based) setting where an entity's public key can be derived from that entity's identity information. From a more practical standpoint, however, one would like to use the primitive signcryption in the hierarchical ID-based setting. In this paper, we propose the first key-evolving hierarchical ID-based signcryption scheme that is scalable and joining-time-oblivious and allows secret keys to be updated autonomously. The security proofs of our construction depend on the bilinear Diffie–Hellman assumption and the computational Diffie–Hellman assumption in the random oracle model. To be specific, our proposal not only achieves the fundamental goals of confidentiality and authenticity, but also enjoys desirable properties of non-repudiation, ciphertext anonymity and strong forward security. Compared with the conventional sign-then-encrypt approach, our construction provides better efficiency in terms of the computation cost and the communication overhead.

Secure group communication with efficient self‐organising key agreement protocol and small broadcast ciphertext is essential to many collaborative and distributed applications in mobile ad‐hoc networks (MANETs). In this study, the authors propose a Chinese Remainder Theorem‐based secure group communication scheme which is able to provide confidentiality service and non‐repudiation service simultaneously. In the proposed scheme, all group members contribute their own public keys to negotiate a shared encryption public key, which corresponds to all different decryption keys. By using the shared public key and the respective secret key, confidentiality and non‐repudiation can be obtained, respectively. Both are essential to secure group communication in MANETs. Compared with the exiting signcryption schemes for multiple recipients, the proposed self‐organising scheme has much smaller ciphertext that does not increase with the size of the group. Accordingly, it is quite suitable to secure group communication in self‐organising, distributed and resource‐constrained MANETs.

ABSTRACT A certificateless public key cryptosystem can make a special contribution to building key distribution and management architecture in resource‐constrained mobile ad hoc networks (MANETs) because it has no separate certificate and no complex certificate management problems. In this paper, we present a virtual private key generator (VPKG)‐based escrow‐free certificateless public key cryptosystem as a novel combination of certificateless and threshold cryptography. Using secret sharing, we build a VPKG whose members collaboratively calculate the partial private key and send it to the user via public channels. The private key of a user is generated jointly by the VPKG and the user. Each of them has “half” of the secret information about the private key of the user. In addition, binding a user's public key with its identity and partial private key, respectively, raises our schemes to the same trust level as is enjoyed in a traditional public key infrastructure. We also show that the proposed scheme is secure against public key replacement attacks and passive attacks. Copyright © 2012 John Wiley & Sons, Ltd.

With cloud storage services, it is commonplace for data to be not only stored in the cloud, but also shared across multiple users. However, public auditing for such shared data - while preserving identity privacy - remains to be an open challenge. In this paper, we propose the first privacy-preserving mechanism that allows public auditing on shared data stored in the cloud. In particular, we exploit ring signatures to compute the verification information needed to audit the integrity of shared data. With our mechanism, the identity of the signer on each block in shared data is kept private from a third party auditor (TPA), who is still able to verify the integrity of shared data without retrieving the entire file. Our experimental results demonstrate the effectiveness and efficiency of our proposed mechanism when auditing shared data.

Machine Type Communication (MTC), as one of the most important communication approaches in the future mobile communication, has drawn more and more attention. To meet the requirements of low power consumption of devices and mass device transmission is the key issue to achieve MTC applications security in the Long Term Evolution (LTE) networks. When a large number of MTC devices simultaneously connect to the network, each MTC device needs to implement an independent access authentication process according to the current third Generation Partnership Project (3GPP) standard, which will cause a severe signaling congestion in the LTE network. In this paper, we propose a group-based access authentication scheme, by which a good deal of MTC devices can be simultaneously authenticated by the network and establish an independent session key with the network respectively. The experimental results show that the proposed scheme can achieve robust security with desirable efficiency and avoid the signaling overload over the LTE networks.

To achieve seamless handovers between the Evolved Universal Terrestrial Radio Access Network (E-UTRAN) and other access networks is a challenging task as it requires comprehensive real-time interconnectivity in the LTE networks. The third Generation Partnership Project (3GPP) has suggested support the mobility between the E-UTRAN and non-3GPP access networks, which requires full access authentication procedures and distinct procedures for different mobility scenarios, which will bring a lot of message exchanges and increase the system complexity. Besides, the existing handover schemes for other wireless networks are not suitable for the mobility scenarios in the LTE networks due to their inherent vulnerabilities. In this paper, we propose a fast and secure handover authentication scheme to fit in with all of the mobility scenarios in the LTE networks. Compared with other handover schemes, our scheme cannot only provide strong security guarantees including Perfect Forward Secrecy (PFS) and Master Key Forward Secrecy (MKFS) and user anonymity, but also achieve a simple authentication process with robust efficiency in terms of communication cost, storage cost and computational cost. The analysis results show that the proposed scheme is efficient and secure against various malicious attacks.

Groups are becoming one of the most compelling features in both online social networks and Twitter-like micro-blogging services. A stranger outside of an existing group may have the need to find out more information about attributes of current members in the group, in order to make a decision to join. However, in many cases, attributes of both group members and the stranger need to be kept private and should not be revealed to others, as they may contain sensitive and personal information. How can we find out matching information exists between the stranger and members of the group, based on their attributes that are not to be disclosed? In this paper, we present a new group matching mechanism, by taking advantage private set intersection and ring signatures. With our scheme, a stranger is able to collect correct group matching information while sensitive information of the stranger and group members are not disclosed. Finally, we propose to use batch verification to significantly improve the performance of the matching process.

ABSTRACT Authentication and key distribution (AKD) protocols become more and more important in the design of wireless networks. Especially, the communication efficiency and security are the critical factors. In this paper, we first analyse the vulnerabilities of an AKD protocol for wireless networks under three types of attacks. Then, we propose an enhanced AKD protocol to overcome those vulnerabilities with the security functionality to prevent those malicious attacks. Security analysis and formal verification mainly using Automated Validation of Internet Security Protocols and Applications toolkit show that the proposed protocol is secure against those attacks. Copyright © 2011 John Wiley & Sons, Ltd.

ABSTRACT Authentication and key distribution (AKD) protocols have become more important in the design of communication systems. The design criteria of the AKD protocols include the scalability, the communication efficiency, the computational efficiency, and the robustness of security. In this paper, we first analyze the vulnerability of an AKD protocol under the off‐line guessing attack. Then, we propose an enhanced AKD protocol to overcome the vulnerability. Security analysis and formal verification by using AVISPA toolkit show that the proposed protocol can keep all the previous properties and is secure against the off‐line guessing attack. Copyright © 2011 John Wiley & Sons, Ltd.

To achieve seamless handovers between the Evolved Universal Terrestrial Radio Access Network (E-UTRAN) and other access networks is a challenging task as it requires comprehensive real-time interconnectivity in the LTE networks. The third Generation Partnership Project (3GPP) has suggested support the mobility between the E-UTRAN and non-3GPP access networks, which requires full access authentication procedures and distinct procedures for different mobility scenarios, which will bring a lot of message exchanges and increase the system complexity. Besides, the existing handover schemes for other wireless networks are not suitable for the mobility scenarios in the LTE networks due to their inherent vulnerabilities. In this paper, we propose a fast and secure handover authentication scheme to fit in with all of the mobility scenarios in the LTE networks. Compared with other handover schemes, our scheme cannot only provide strong security guarantees including Perfect Forward Secrecy (PFS) and Master Key Forward Secrecy (MKFS) and user anonymity, but also achieve a simple authentication process with robust efficiency in terms of communication cost, storage cost and computational cost. The experimental results and formal verification by using the TLA+ and TLC show that the proposed scheme is secure against various malicious attacks.

Security, anonymity, and scalability are important issues for mobile ad hoc network routing protocols, especially in private-vital practical applications. Although there are several papers on anonymous secure routing in mobile ad hoc networks, few research works can achieve sufficient anonymity and strong security, and at the same time provide good scalability. Therefore, in this paper, we present a new distributed anonymous secure routing protocol with good scalability for mobile ad hoc networks. The proposed protocol addresses the problems of anonymous routing and later anonymous data transmission via a dynamic identity pseudonymity approach based on Incomparable Public Keys. Good scalability is achieved by using Diffie-Hellman key exchange scheme and symmetric key cryptography, instead of public key cryptography. In comparison with other previous anonymous secure routing protocols, the proposed protocol not only has strong security and sufficient anonymity protection, but also provides better scalability.

A secure and efficient ID-based registration protocol with user anonymity is proposed in this paper for IP-based mobile networks. The protocol minimizes the registration delay through a minimal usage of the identity (ID)-based signature scheme that eliminates expensive pairing operations. User anonymity is achieved via a temporary identity (TID) transmitted by a mobile user, instead of its true identity. Additional replay protection from a Foreign Agent (FA) is included in the registration messages to prevent a possible replay attack. A formal correctness proof of the protocol using Protocol Composition Logic (PCL) is presented. Numerical analysis and computer simulation results demonstrate that the proposed protocol outperforms the existing ones in terms of the registration delay, the registration signaling traffic, and the computational load on a Mobile Node (MN) while improving security. For example, the proposed protocol reduces the registration delay up to 49.3 percent approximately, comparing to Yang's protocol.

Temporal logics of knowledge are useful for reasoning about situations where we require both dynamic aspects and informational aspects relating to knowledge. So, the application of temporal logics of knowledge is investigated to the specification and verification the properties of TLS protocol. Two main properties of TLS are given out. And the definitions and lemmas for the proof of these properties are also investigated. Finally, several other kinds of logics which is used to specify and analyze security protocols are discussed.

Kerberos protocol is a famous identity authentication protocol and it is widely used in the network as a standard. But there is still not a strict proof of it base on the Formal method. That is very nervous for the users. So a security analysis of the Kerberos protocol using BAN logic is proposed in this paper, and the reliability, practicability and security of Kerberos protocol are proved.

To solve the problem of the abuse in electronic payment protocols, a new abuse-free fair electronic payment protocol is proposed in this paper. The proposed protocol solves the problems in Parks' protocol and furthermore it solves the abuse using off-line trusted third party (TTP). The security and fairness are also possessed in the protocol.

Providing anonymous routing in mobile ad hoc networks (MANET) has been a hot issue for the purpose of security and privacy concerns. But there are very few have been done about providing a valid method to detect malicious node and providing a trustworthy protection over whole network. In this paper, we introduce a localized trust management which can primarily remove malicious nodes, and propose an efficient anonymous routing protocol by node that participates in the protocol encrypts entire message with trust key and says Hello to its ancestor within expiration time. It makes malicious node can be detected and isolated from the network. In this way, a anonymous and secure route path can be established in a hostile environment. Meanwhile, it is able to efficiently against the Denial-of-Service (DoS) attack.

A secure and efficient mobile IP (MIP) registration protocol using certificateless signature scheme is proposed. The protocol minimises the registration time through minimal usage of an efficient certificateless signature scheme between a foreign agent (FA) and a home agent (HA). Protocol parameters can be kept resynchronised by reusing the initial values in the MIP registration in case the synchronisation between a mobile node (MN) and HA is lost. User anonymity is achieved via a temporary identity transmitted by a mobile user, instead of its true identity. Additional replay protection from the FA is included in the registration messages to prevent a possible replay attack. Numerical results and performance analyses demonstrate that the proposed protocol outperforms the existing ones in terms of the registration time, registration signalling traffic and computational load on an MN while providing improved security. For example, the proposed protocol reduces the registration time up to ∼83% compared with the protocol from Yang.