Data protection laws and regulations require service providers to disclose data practices in privacy policies, specifying what personal information is processed and for what purposes. For compliance, these data practices must adhere to the data minimization principle, limiting the processing of personal information to what is directly relevant and necessary for the service purposes. However, data minimization is context-dependent, making violations difficult to define and quantify in privacy policies. Meanwhile, privacy policies are semantically complex and unstructured, hindering accurate extraction of fine-grained data practices and large-scale automated evaluation. To address these issues, we propose DataMini, a human--LLM collaborative evaluation framework for identifying violations of the data minimization principle in privacy policies. First, DataMini categorizes data minimization violations into two dimensions: inherent violations and contextual violations, establishing fine-grained evaluation criteria. Second, we construct a compliance baseline by mining high-frequency patterns from large-scale privacy policies and integrating expert knowledge to derive compliance mappings for human--LLM collaborative evaluation. Finally, the compliance baseline can automatically verify data practices that satisfy the data minimization principle, enabling the framework to focus exclusively on identifying suspected violations to improve efficiency and accuracy. Extensive evaluations demonstrate that DataMini exhibits superior data practice extraction accuracy of 83.46% and achieves an F1-score of 0.8180 for identifying data minimization violations in privacy policies, reducing manual evaluation effort by approximately 80%.

Machine learning (ML) based classifiers that take a privacy policy as the input and predict relevant concepts are useful in different applications such as (semi-)automated compliance analysis against requirements of a specific data protection law such as the EU GDPR. Although many researchers have studied ML-based privacy policy concept classifiers, we observed multiple research gaps, e.g., the lack of a more complete GDPR taxonomy and the less consideration of hierarchical information in privacy policies. To fill such research gaps, we produced a more complete GDPR-oriented privacy policy concept taxonomy, constructed the first privacy policy corpus with explicitly hierarchical information at three levels, and conducted the most comprehensive performance evaluation study of GDPR concept classifiers for privacy policies, cover many aspects that have not been studied systematically. Our work led to multiple findings and insights, including the usefulness of considering hierarchical contextual features and different hierarchical structures, the observation that a “one size fits all” approach may not work, the reduced performance of such classifiers on our newly constructed corpus especially after the first level, and the necessity to split the training and testing sets by documents.

As a fundamental task in graph data analysis, degree statistic estimation serves as the foundation for many complex tasks. Local differential privacy (LDP) preserves the privacy inherent in raw degrees without a trusted third party. Existing methods struggle to balance different types of degree statistics. They either introduce excessive noise when estimating degree distribution due to not fully leveraging the properties of edge LDP, or are limited to polynomial statistic estimation only. We design a locally private framework for degree statistic estimation (LFS), using Laplace mechanism to provide appropriate privacy protection under edge LDP. LFS can estimate three types of degree statistics: polynomial, distribution and single-point. According to degrees with Laplace noise, we transform degree distribution estimation into a linear regression problem, then post-process the estimated distribution to mitigate the excessive smoothing introduced by the regularization term. We also achieve single-point statistic estimation considering the degree distribution and properties of Laplace noise. Systematic experiments on five datasets demonstrate that LFS consistently outperforms existing methods in four utility metrics.

Private protocols are widely used on the network to improve efficiency and protect privacy. However, it lacks standard protocols to unify the communication process and improve security. Protocol Reverse Engineering (PRE) aims to infer the syntax, semantics, and timing of the unknown protocol. In detail, it always infers the field information and the state machine. Yet, the results of existing PRE methods focus byte-level, and the target setting is an empirical process. To overcome the shortage, we proposed BitInfer for binary protocols. BitInfer uses a set of field detectors, including Length Detector, Timestamp Detector, Entropy Detector, Sequence Detector and Reserve Detector to divide the protocols into bit-level field and get the confidence of them. Then we randomly choose the combination of detector tuples to get the init population by repeating the process for a specific times. Finally, we use NSGA2 as the intergrade algorithm to deal with the field conflict. Experiments show that the result of BitInfer precisely solve the conflict and extend to bit level.

Proprietary protocols are widely used to ensure efficient data transmission, enhance privacy, and meet specific application requirements. However, the lack of public standards often leaves their security inadequately evaluated, posing significant challenges for network security. Protocol Reverse Engineering (PRE) is used to analyze protocols by inferring their structure and behavior. However, existing PRE methods primarily focus on protocol format analysis, neglecting Protocol State Machine (PSM) analysis, which can lead to insufficient detection of abnormal behaviors and potential vulnerabilities. To address this, we propose an automatic PSM inference framework for unknown protocols, incorporating a fuzzy membership-based auto-converging DBSCAN algorithm for protocol format clustering, followed by a session clustering algorithm based on Needleman-Wunsch and K-Medoids algorithm to classify sessions by protocol type. Finally, we refine a probabilistic PSM algorithm to infer protocol states and transitions. Experiments show that our method can infer PSMs while enabling precise protocol classification.

LEO satellite non-terrestrial networks (NTN) utilize satellites in Low Earth Orbit (LEO) to dynamically establish global communication service and own significant promise. The dynamic nature of LEO satellite NTN necessities efficient handover authentication protocols. However existing schemes cannot be directly applied in LEO satellite NTN because of their low efficiency and security. To address these problems, we propose a handover authentication protocol to quickly and securely authenticate the user’s identity during the handover process. In our scheme, we incorporate an implicit session-bound random challenge to facilitate mutual authentication and key agreement between the User Equipments (UEs) and satellites. To improve authentication efficiency, we propose a batch handover mechanism to transfer the necessary security contexts, largely reducing the handover authentication cost. We verify our protocol’s security using BAN logic and Tamarin prover. The performance evaluation shows that SEHAP outperforms other schemes in both communication and computational efficiency in LEO satellite NTN.

In end-to-end data sharing, data are directly distributed to data receivers and stored on their terminals, making it hard to ensure forward security because receivers whose permissions have been revoked may still access previously shared data. To address these challenges, we propose an attribute-key-free CP-ABE scheme, aimed at securely binding data with access policies while ensuring forward security. Specifically, the decryption process in our scheme is delegated to the attribute authorities, which adopt the user’s real-time attribute values to decrypt the ciphertext. To prevent the honest-but-curious attribute authorities from accessing the plaintext, the ciphertext is re-encrypted with a one-time key before being sent to the attribute authorities. Furthermore, to prevent sensitive information from being inferred through the policy, we design a policy-hiding mechanism to conceal attribute values. Through these mechanisms, it can be ensured that the data subject always has control over his or her personal data during the end-to-end data-sharing process. We evaluate the performance of our scheme through both theoretical analysis and comparative experiments, and the results show our scheme’s effectiveness.

The Satellite Computing System (SCS) faces an increasing number of attacks. Although Moving Target Defense (MTD) can effectively mitigate attacks in ground networks, it is not well-suited for SCS due to the highly dynamic nature of both SCS traffic and attackers’ scanning behaviors. In this paper, we propose a dynamic MTD strategy optimization scheme using Advantage Actor-Critic (A2C) reinforcement learning. Specifically, we formulate the MTD strategy optimization for SCS as a Markov Decision Process (MDP). Furthermore, by accounting for the uncertainty in attack behavior changes, we apply A2C reinforcement learning to optimize the MTD strategy within the MDP framework. Experimental results demonstrate that our scheme effectively reduces the frequency of scanning hits, shortens the duration attackers can hold addresses, and minimizes the impact of MTD on quality of service.

Space-Ground Integrated Networks (SGIN) devices are at risk of hardware Trojan attacks. Currently, existing detection schemes (e.g., deep learning) require a large amount of labeled samples. However, obtaining a high-quality labeled hardware Trojan dataset for SGIN devices is challenging due to the structural complexity of hardware, resulting in poor detection performance. To address this challenge, this paper combines prompt-tuning with LangChain to propose a hardware Trojan detection scheme for SGIN devices without requiring extensive training samples. In our scheme, we transform hardware Trojan detection into a mask prediction problem and design a two-phase prompt-based detection framework. In the first phase, we design 5 prompt patterns with masks and utilize Roberta-large as a large language model (LLM) to predict masks and their confidence. If their confidence is below a given threshold value, the second phase is initiated, where the corresponding original samples are fed into LangChain to optimize detection. To enhance the detection accuracy, we develop a Positional State Tree (PST) to extract the logical parallel structure of SGIN Trojan. Experiments show that our scheme achieves an accuracy of 91.3% in detecting the presence of Trojans and 96.3% in identifying the types of Trojans, respectively.

The emergence of text-to-image models has recently sparked significant interest, but the attendant is a looming shadow of potential infringement by violating user terms. Specifically, an adversary may exploit data created by a commercial model to train their own without proper authorization. To address such risk, it is crucial to investigate the attribution of a suspicious model's training data by determining whether its training data originates, wholly or partially, from a specific source model. To trace the generated data, existing methods need to apply additional watermarks during either the training or inference phases of the source model. However, these methods are impractical for pre-trained models that have been released, especially when model owners lack security expertise. To tackle this challenge, we propose an injection-free training data attribution method for text-to-image models. It can identify whether a model's training data stems from a certain source model without adding additional watermarks on the source model. The rationale of our method lies in the inherent memorization characteristic of text-to-image models. The memorization of training data is inherited through the data generated by the source model to the model trained on that data, making the source model and the infringing model exhibit consistent behaviors on specific samples. Therefore, from instance-level, we develop detection-based and generation-based strategies to uncover these distinct samples and using them as inherent watermarks to verify if a suspicious model originates from the source model. Besides, we also propose a statistical-level attribution method, utilizing the shadow model technique to train an attribution discriminator. Experiments demonstrate that the attribution accuracy and AUC scores of our methods are over 80% even when the infringing model only uses a small proportion of generated data.

Meteorological images typically contain sensitive and critical information, demanding effective security mechanisms to prevent unauthorized copying and distribution. Digital watermarking, a widely adopted protection technique, imperceptibly embeds user identity information into images to enable subsequent traceability. However, existing methods fail to consider the impact of watermark embedding on critical meteorological features, as well as the limitations of high computational overhead and low embedding efficiency in multi-user distribution scenarios. To address these issues, we propose a watermarking framework tailored for the secure distribution of meteorological images. Specifically, we first design a content-adaptive region selection scheme that avoids embedding watermarks in critical meteorological regions such as cloud features. We then develop a two-stage decoupled embedding strategy to enhance distribution efficiency, where the preprocessing stage performs region selection and frequency transformation, while the distribution stage reuses these cached results to rapidly generate user-specific watermarked copies. Extensive experimental results demonstrate that our framework effectively avoids critical regions in meteorological images and maintains high visual quality (average PSNR of 38.10 dB and SSIM of 0.9815) while achieving better extraction accuracy under various attacks. Furthermore, the two-stage decoupled embedding strategy reduces server response latency by over 60.8% in concurrent distribution scenarios.

Hardware Trojans pose a significant security risk in space-ground integrated network (SGIN) devices. It is widely accepted in academia and industry that detecting hardware Trojans at an early stage, typically in register transfer-level (RTL) hardware design, can effectively protect the SGIN device. However, the few hardware Trojan samples dedicated to SGIN (called sHT) make it difficult to detect them using deep learning. To obtain more sHT samples automatically and quickly, this article proposes a lightweight automatic sample augmentation framework for hardware Trojan (HT-ASAF). In our scheme, we first designed a lightweight neural network called variational autoencoder for hardware Trojan (HT-VAE) to achieve high-generation quality without a large amount of training data. Further, we develop the positional state tree (PST) and introduce a node tuple representation for interconversion between PST and sequence to capture the intricate semantic features of concurrent operations in hardware design to enhance the performance of HT-VAE. To automatically verify the effectiveness of the augmented samples, we established an experimental platform incorporating cluster mapping (CLM), which can reduce the verification complexity. In our experiments, to obtain a small number of the training hardware Trojan samples for SGIN, we added activation mechanisms, such as velocity or altitude, to the existing RTL hardware Trojans samples to simulate the hardware Trojan threats faced by orbit devices. The set of the obtained samples is called sHT dataset. Experimental results on the obtained sHT dataset demonstrate that HT-ASAF can automatically and efficiently augment hardware trojan sample compared to existing augmentation schemes, and it performs well in the downstream task of hardware Trojan detection on SGIN devices.

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.

Multi-range query (MRQ) is a typical multi-attribute data query widely used in various practical applications. It is capable of searching all data objects contained in a query request. Many privacy-preserving MRQ schemes have been proposed to realize MRQ on encrypted data. However, existing MRQ schemes only consider the security threat caused by access pattern leakage, not the harm of volume pattern leakage. Moreover, most existing schemes cannot achieve efficient queries and updates while preserving the access pattern. In this paper, we propose an efficient MRQ scheme for hiding volume and access patterns. We first design a joint data index using Order-Revealing Encryption (ORE) and Pseudo-random functions (PRFs) to realize volume-hiding range queries. Then, we combine the private set intersection (PSI) and hardware Software Guard Extensions (SGX) to compute each attribute’s intersection of query results. In addition, we preserve access patterns during queries by designing a batch refresh algorithm and an update protocol. Finally, rigorous security analysis and extensive experiments demonstrate the security and performance of our scheme in real-world scenarios.

Multi-dimensional range query (MRQ) over outsourced data has been extensively applied in various domains. However, security and efficiency are still two aspects that cannot be easily balanced in private MRQs, as improving security inevitably incurs high computation, storage, and communication costs. Several schemes perform encrypted data retrieval in the trusted execution environment (TEE), which balances security and performance. Unfortunately, they focused on keywords or single-dimensional range queries, failing to address private MRQs. With the TEE (i.e., Intel SGX), we propose a response-hiding MRQ scheme over encrypted data (SGX-MRQ) in this paper. We first design an index structure called SDic, which can achieve efficient range queries while hiding the responses to each query from the server. Moreover, based on the security properties of SGX, we construct the encrypted polynomials of each dimension on the enclave and implement the intersection computation of multi-attribute queries by the server, which greatly improves the system efficiency. We present the formal definition of SGX-MRQ and perform a rigorous proof. We implement a prototype of SGX-MRQ and conduct extensive experiments on real datasets. The evaluation results validate the feasibility of our scheme in practical applications.

The completion of missing network traffic is of great significance for network operation and maintenance. In recent years, the low-rank tensor completion (LRTC) techniques based on tensor ring (TR) decomposition have attracted much attention. In general, the LRTC model requires the stability of the whole tensor space. However, continuous mutation of network traffic is very common in real networks. At this time, existing completion work has difficulty in capturing the global low-rank feature of normal data and ignores the local continuous feature of mutation data, leading to a decrease in completion performance. To solve the above problems, we propose a low-rank tensor completion model that can adapt to various continuous mutation patterns of network traffic. The original tensor is represented as the sum of a normal tensor and a mutation tensor to extract their features respectively. Then, an algorithm based on the alternating direction method of multipliers (ADMM) is developed to solve the proposed model. Finally, our experimental results on both synthetic and real datasets indicate that our model can adapt to various missing data completion under different continuous mutation patterns, and has more accurate completion performance compared to advanced models.

Access control policies play a critical role in securing sensitive data and protecting personal rights in environments such as cloud computing and IoT. These policies, typically created by sysadmins, specify which users are authorized to access specific resources under certain conditions. However, the manual creation and revision of these policies to align with security objectives is often error-prone and labor-intensive. In this paper, we present Custominer, a policy mining tool designed to assist sysadmins in proactively generating and customizing access control policies that meet predefined security requirements. Custominer enables sysadmins to define security goals as constraints, and then automatically mines policies that satisfy these constraints from access logs. The policy mining task is framed as a local search optimization problem, utilizing a MaxSAT solver to efficiently eliminate suboptimal policy candidates. Our experiments, conducted on four real-world datasets, show that Custominer outperforms existing state-of-the-art methods in terms of both accuracy and efficiency.

As cross-domain access constitutes a significant portion of network communication, multi-domain networks present both enhanced capabilities and increased cybersecurity risks. Traditional defense strategies often overlook the complexities of cross-domain collaboration, particularly the strategic interactions among domains that prioritize their own interests. In this paper, we introduce Macd, a multi-domain collaborative defense framework, which models the defense interactions as a multi-agent stochastic game. This enables Macd to consider long-term security performance across domains, mitigating multi-step attack threats. To promote effective collaboration, we propose a Shapley-value based reputation mechanism to ensure fair incentives for non-attacked domains that contributing Security Service Functions (SSFs). Additionally, we implement a MAPPO-based Macd-solver to dynamically compute optimal defense strategies. Simulations in a DDoS attack-defense scenario demonstrate that Macd significantly enhances cross-domain collaboration and improves the overall security of multi-domain networks.

A deep learning model can be vulnerable to a membership inference attack (MIA) which allows an attacker to determine if a specific data record was used for its training. In this paper, we investigate the unfairness of disparate vulnerability to MIA across different subgroups in terms of their data distributions. We propose three practical methods to characterize the distribution of complex training data for deep learning models, which are validated to be effective in identifying the vulnerable data records. We then provide a theoretical definition for MIA vulnerability. Experimental results demonstrate the impact of data distribution on disparate vulnerability, where the out-of-distribution outliers are much more easily attacked than normal data records. Even if the accuracy of MIA looks no better than random guessing over the whole population, there are certain groups of "outliers" can be significantly more vulnerable than others. For example, the attack accuracy on examples with the largest 10% outlierness is 15% higher than that on in-distribution examples.

Edge computing is witnessing a convergence of cloud data centers and edge clouds, thereby the large thereby intensifying the vulnerability of cloud services from multi-vector DDoS attacks. However, existing DDoS filtering approaches, characterized by independent offline decisions made by clouds, exhibit shortcomings in efficacy and real-time performance. This paper proposed an online collaborative mitigation framework for multi-vector DDoS attacks, which formulates the mitigation challenge as an Online Multi-dimensional Multiple-Choice Knap-sack Problem (O-MdMCKP). Further, the framework generates candidate filtering policies for each incoming attack flow and designs a policy selection algorithm by employing online analysis based on reservation functions, ensuring prompt and efficient filtering. Experimental results show the proposed algorithm outperforms other online benchmark methods.

In recent years, with the remarkable development of Virtual Power Plants (VPP) and the surge in the number of Electric Vehicles (EVs), the issue of data privacy leakage has become increasingly prominent. The effectiveness of existing federated learning schemes in mitigating data privacy leakage, it still faces potential threats such as inference attacks and user and server collusion. To protect the privacy of federated learning, some schemes have introduced differential privacy(DP). Nevertheless, applying DP will inevitably affect the accuracy to some extent. In this paper, we propose an adaptive differential privacy scheme for VPP based on federated learning, named ADP-VFL. Our ADP-VFL scheme can achieve data privacy preservation by transmitting the noise-added data as a chain, defend against inference attacks by innovating offset noise mechanism and a parallel transmission scheme. The performance evaluation results demonstrate that the proposed scheme can improve the aggregation accuracy and reduces the communication overhead.

The space-ground integrated networks (SGIN) is vulnerable to complex and evolving threats due to its open nature. However, the dynamic topology and limited resources of SGIN present significant challenges for security resource orchestration. Most existing studies focus on network function orchestration and resource allocation for service flows, overlooking the offensive and defensive characteristics of security resource orchestration. Moreover, they fail to adequately address the difficulties posed by the dynamic topology of SGIN. To address these gaps, we utilize a virtual node method and network structure characteristics to transform the dynamic network topology into a static scale-free network. The orchestration strategy generation problem is then modeled as a minimum spanning tree truncation game on the network. Given the NP-hard nature of the problem, we propose the OSG algorithm based on Benders decomposition to solve it. To further improve the OSG algorithm’s efficiency, we introduce an initial value algorithm and four cutting plane inequalities, culminating in the AOSG algorithm. Extensive experiments conducted on networks of varying scales demonstrate that the AOSG algorithm, incorporating the initial feasible solution and Hamming inequality, delivers superior performance and generates optimal orchestration strategies within a feasible time frame.

In an increasingly interconnected world, multi-domain networks serve as vital infrastructure, enabling seamless communication and resource sharing across diverse sectors, but also leading to increasingly frequent cyberattacks. Defense-in-depth (DiD) is widely regarded as a necessary strategy for mitigating these threats through layered security measures. However, current DiD strategies often fall short due to their single-domain focus, reliance on centralized control, and inability to adapt to dynamic threats. This paper proposes a novel framework to extend DiD strategies for multi-domain networks. It progressively defines key elements of multi-domain networks, culminating in a detailed hierarchical framework that clarifies the roles and interactions of management domains. Furthermore, cross-domain intrusion response is modeled as a multi-agent stochastic game, accounting for self-interested behavior and interactions between domains. The Independent Q-Learning (IQL) algorithm is employed to solve this game, with experimental results demonstrating substantial improvements in security across multi-domain environments.

The introduction of satellite Internet resource-slicing technology can efficiently allocate satellite network resources and meet the personalized needs of different users. This article proposes a trust-based satellite Internet resource-slicing access authentication scheme, which solves the efficient and secure access requirements in situations where satellite communication and service resources are relatively limited. The working idea of this article is to provide users with access authentication protocols with different efficiencies through trust as a standard. Firstly, The user’s trust value is calculated by establishing a trust metric model based on Beta function, communication byte fluctuations, and centralized trend measurements. Drawing on the requirements of the security policy function in the resource slicing technology standard, assigning different security policies to users can both improve the fast access ability of high-trust users and reduce the priority of low trust users’ access. After that, based on the results of trust metrics, this paper proposes a two-factor-based no certificate satellite Internet slicing access authentication protocol for users with moderate trust levels. This protocol achieves the ability for users to access slicing services anonymously and efficiently through the use of resource-slicing credentials and managers. Final, this article verify the correctness and security of the protocol. Through communication cost comparison, it is shown that this protocol has fewer costs. Through trust simulation, the effectiveness of the trust scheme is analyzed and compared.

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.

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.

Federated learning (FL) is a distributed learning framework that can reduce privacy risks by not directly sharing private data. However, recent works have shown that the adversary can launch data reconstruction attacks utilizing the gradients or model updates shared by clients. Existing defenses either fail to provide sufficient privacy guarantee or incur significant drop in model accuracy. To achieve a good privacy-utility tradeoff, we propose a novel block-wise pruning method. It mitigates the privacy leakage by locating and quantifying the privacy risk of a model at a finer-grained level. Specifically, we define the sensitivity metric to calculate the gradient sensitivity w.r.t the input to quantify privacy leakage risk of each block. Then we divide the entire model into same-sized blocks and sort them based on the sensitivity metrics. We select part of the blocks with least sensitivity values as the pruned model to be communicated during the client-server interaction. To evaluate the effectiveness and efficiency of our defense, we conduct experiments on MNIST and CIFAR10 for defending against the DLG attack and GS attack. Results demonstrate that our proposed method can significantly mitigate gradient leakage against both DLG attack and GS attack with as much as 20× mean squared errors between the reconstructed data and the raw data with only modest accuracy drop, compared with baseline defenses. Meanwhile, the communication cost between the server and clients is also reduced.

Adversarial learning is commonly used to extract latent data representations which are expressive to predict the target attribute but indistinguishable in the privacy attribute. However, whether they can achieve an expected privacy-utility tradeoff is of great uncertainty. In this paper, we posit it is the complex interaction between different attributes in the training set that causes disparate tradeoff results. We first formulate the measurement of utility, privacy and their tradeoff in adversarial learning. Then we propose the metrics of Statistical Reliability (SR) and Feature Reliability (FR) to quantify the relationship between attributes. Specifically, SR reflects the co-occurrence sampling bias of the joint distribution between two attributes. Beyond the explicit dependence, FR exploits the intrinsic interaction one attribute exerts on the other via exploring the representation disentanglement. We validate the metrics on CelebA and LFW dataset with a suite of target-privacy attribute pairs. Experimental results demonstrate the strong correlations between the metrics and utility, privacy and their tradeoff. We further conclude how to use SR and FR as a guide to the setting of the privacy-utility tradeoff parameter.

Abstract As mobile communications, the Internet, databases, distributed computing, and other technologies continue to develop, the Internet of Things (IoT) has emerged as prevalent technique. However, attacks on security and sensitive data in IoT occur frequently, and these attacks often evade intrusion detection systems strategically by mutating their traffic. To prevent security threats and sensitive data leakage, we propose a game approach based on adversarial deep learning to optimize a dynamic security threshold strategy. We introduce a mobile edge computing framework and utilize a game model to describe the adversarial interaction between the two participants. To solve the complexity of the game problem to gain dynamically randomized adversarial attacks, we present a column generation (CG) framework, which uses a feedforward neural network to quantify data flowing through IoT devices. Considering the limited resources of IoT devices, we calculate an optimal response to cyberattacks via a particle swarm optimization algorithm, aiming to reduce the false alarm rate. The adversarial dynamic threshold (ADT)‐based column generation (CG‐ADT) algorithm generates the set of detection threshold and the probability. Finally, we present the results of experiments conducted to demonstrate the effectiveness and robustness of the proposed dynamic threshold scheme for sensitive data security protection in IoT and its suitability for implementation in production systems.

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.

Insiders cause serious security threats to organizations. Existing insider threat detection methods mainly mine the users' behaviors or psychological features by analyzing the users' operation logs, and they ignore the associations of behaviors among users and get unappealing performance on the imbalanced samples. In this paper, considering attention mechanism, we propose Generative Adversarial Graph Attention Networks (GAGAN) to detect insider threats. First, we design association rules to construct a graph to associate users' behaviors. Second, to address the imbalanced samples, we adopt graph generator to generate abnormal nodes; A discriminator with graph attention networks is designed to further mine the potential associations of behaviors among users and discriminate real nodes from the generated nodes, also adopted to discriminate anomaly nodes from normal nodes. Experimental results on CERT data set demonstrate that our method can accurately detect abnormal insiders and outperforms several state-of-the-art baseline methods.

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.

Hybrid distributed denial-of-service (DDoS) attack, which utilizes multiple types of DDoS attack to launch one attack event, has become more rampant. However, existing researches for DDoS attack detection mainly focus on the single attack scene and ignore the hybrid attack incident. To deal with the hybrid DDoS attack detect problem, we propose a distributed and collaborative DDoS detection framework(DICOF) to detect and classify multiple DDoS attack simultaneously. Firstly, we propose an entropy-based method to quickly identify the DDoS attack events by measuring the distribution of the total length of inbound and outbound packets for network traffics. Then, we adopt a GRU(Gated Recurrent Unit) based classification method to distinguish the type of different DDoS attacks contained in one attack event. Experiment results show that the DICOF is able to detect hybrid DDoS attack events at millisecond level and classify different DDoS attacks precisely.

Accurately constructing dynamic network topology is one of the core tasks to provide on-demand security services to the ubiquitous network. Existing schemes cannot accurately construct dynamic network topologies in time. In this paper, we propose a novel scheme to construct the ubiquitous network topology. Firstly, ubiquitous network nodes are divided into three categories: terminal node, sink node, and control node. On this basis, we propose two operation primitives (i.e., addition and subtraction) and three atomic operations (i.e., intersection, union, and fusion), and design a series of algorithms to describe the network change and construct the network topology. We further use our scheme to depict the specific time-varying network topologies, including Satellite Internet and Internet of things. It demonstrates that their communication and security protection modes can be efficiently and accurately constructed on our scheme. The simulation and theoretical analysis also prove that the efficiency of our scheme, and effectively support the orchestration of protection capabilities.

With the help of satellites, Internet of Things (IoT) applications such as remote monitoring and ocean exploration can be realized. However, the network is vulnerable to malicious attacks due to the limited resources of satellite IoT (S-IoT) terminals and the openness of communication links. Ensuring that legitimate users can only access sensitive data remains a major concern. In this paper, a zero-trust access management model integrated with satellites, network and identity infrastructure, as well as the authentication protocol for S-IoT terminal security, was designed. The protocol adopts the Chinese cryptographic algorithms SM2, SM3, and SM4, combined with the Physical Unclonable Function (PUF) to achieve key agreement and bidirectional authentication. The security of this protocol is further analyzed, and its security, function, and performance are compared with other related protocols. Experimental results show that compared with existing similar schemes, the proposed protocol can more effectively consider the security requirements of S-IoT and reduce communication costs.

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.

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.

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.

Users usually have different privacy demands when they contribute individual data to a dataset that is maintained and queried by others. To tackle this problem, several personalized differential privacy (PDP) mechanisms have been proposed to render statistical information of the entire dataset without revealing individual privacy. However, existing mechanisms produce query results with low accuracy, which leads to poor data utility. This is primarily because (1) some users are over protected; (2) utility is not explicitly included in the design objective. Poor data utility impedes the adoption of PDP in the real-world applications. In this paper, we present an adaptive personalized differential privacy framework, called AdaPDP. Specifically, to maximize data utility in different cases, AdaPDP adaptively selects underlying noise generation algorithms and calculates the corresponding parameters based on the type of query functions, data distributions and privacy settings. In addition, AdaPDP performs multiple rounds of utility-aware sampling to satisfy different privacy requirements for users. Our privacy analysis shows that the proposed framework renders rigorous privacy guarantee. We conduct extensive experiments on synthetic and real-world datasets to demonstrate the much less utility losses of the proposed framework over various query functions.

With the increasingly powerful and extensive deployment of edge devices, edge/fog computing enables customers to manage and analyze data locally, and extends computing power and data analysis applications to network edges. Meanwhile, as the next generation of the power grid, the smart grid can achieve the goal of efficiency, economy, security, reliability, use safety and environmental friendliness for the power grid. However, privacy and secure issues in fog-based smart grid communications are challenging. Without proper protection, customers’ privacy will be readily violated. This article presents a smart and practical Privacy-preserving Data Aggregation (PDA) scheme with smart pricing and packing method for fog-based smart grids, which achieves diversified tariffs, multifunctional statistics and efficiency. Especially, we first propose a smart PDA scheme with Smart Pricing (PDA-SP). With PDA-SP, the Control Center (CC) can compute more complex and higher-order aggregation statistics to provide various services, provide diversiform pricing strategies and choose a double-winning strategy. Subsequently, we put forward a practical PDA scheme with Packing Method (PDA-PM), which is able to reduce the size of encrypted data and improve performance in performing various secure computations. Moreover, we extend our original packing method and present a more useful packing method, which can handle general vectors with large entries. The security analysis shows that our proposed scheme is secure against many threats. The performance evaluation reveals that the computation and communication overheads of our proposed scheme are effectively reduced by employing the Somewhat Homomorphic Encryption (SHE), and our packing method can further significantly reduce these overheads.

Suffered from the contradiction between the limited capacity of local devices and large size of DNN models, a practical solution is transferring the heavy computational tasks from the local to the server side such as cloud. However, the untrusted server naturally requires all the user data to train neural networks and infer results, which causes the asset loss of the local and raises serious privacy concerns on user's sensitive information. To solve this problem in scenarios of machine learning as a service, we propose a general framework to balance the user privacy, model accuracy and training efficiency, simultaneously. Specifically, our representative subset selection algorithm takes the training value of data into account, selecting the most representative subset from the training data, in order to mitigate the loss of data assets, lower down the transmission overhead from the local to the server and lessen the training burden on the server at the same time. We also design a noisy representation transformation algorithm applying on the features extracted by neural networks to further perturb the data within the selected representative subset. Extensive experiments demonstrate that our framework can run locally with little sacrifice on the computation resource. It can not only protect private data before uploading, but also promote the training efficiency of servers.

Generating fine-grained response policies is a fundamental problem for Intrusion Response Systems (IRSs). Although existing schemes determine countermeasures and defense points efficiently, they ignore the deployment orders and execution durations of the selected countermeasures, which may impact response performance. To address this problem, by considering four attributes (i.e., attack damage, deployment cost, negative impact on QoS, and security benefit), we propose a decisionmaking framework for IRSs to reach fine-grained decisions to balance attack damage and response cost. We formulate decisionmaking as a single-objective optimization problem. To efficiently solve this problem, a Genetic Algorithm with Three-dimensional Encoding (GATE) is proposed to not only select countermeasures and defense points, but also determine deployment orders and execution durations. Simulation results demonstrate the efficiency of our approach.

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.

Personalized Differential Privacy (PDP) was proposed to satisfy users' different privacy requirements. However, most of the existing PDP mechanisms may significantly destroy the utility of released statistical results. Differentially private statistical results with poor utility may mislead the data analysts, thus it may even decrease the acceptability of the technique used to protect data privacy. Therefore, in this paper, our goal is to pursue higher data utility while satisfying personalized differential privacy. To achieve this goal, we propose the Utility-aware Personalized Exponential Mechanism (UPEM) to effectively achieve PDP while pursuing better utility. UPEM distinguishes the different possible results with the same personalized score, which is used in Personalized Exponential Mechanism (PEM) [1]. PEM considers the personalized privacy budgets of changing elements to achieve PDP. Based on PEM, our UPEM further considers the quantitative changes of these changing tuples to enhance the utility. We confirm the effectiveness and efficiency of UPEM through extensive experiments.

Summary The publish/subscribe paradigm provides loosely coupled and scalable communication for the Internet of Things (IoT). In this paradigm, access control is an efficient approach to guaranteeing security. However, existing access control methods are not suitable for the publish/subscribe paradigm in the sensing layer of the IoT due to their coarse‐grained controls and lack of self‐configuration. To address these problems, in this paper, we propose a topic‐centric access control model (TCAC) to realize fine‐grained authorization for the sensing layer of the IoT. First, we use topics, a fundamental concept for the publish/subscribe paradigm, as the basic access control unit to dynamically authorize access according to the attributes of devices, users, and topics. Second, an administration model for TCAC is proposed to manage these attributes and configure access policies to effectively implement user‐driven access controls. Finally, a healthcare case is used to demonstrate the security of the proposed TCAC. The results show that our model is dynamic, fine‐grained, and user driven.

Group activities on social networks are increasing rapidly with the development of mobile devices and IoT terminals, creating a huge demand for group recommendation. However, group recommender systems are facing an important problem of privacy leakage on user’s historical data and preference. Existing solutions always pay attention to protect the historical data but ignore the privacy of preference. In this paper, we design a privacy-preserving group recommendation scheme, consisting of a personalized recommendation algorithm and a preference aggregation algorithm. With the carefully introduced local differential privacy (LDP), our personalized recommendation algorithm can protect user’s historical data in each specific group. We also propose an Intra-group transfer Privacy-preserving Preference Aggregation algorithm (IntPPA). IntPPA protects each group member’s personal preference against either the untrusted servers or other users. It could also defend long-term observation attack. We also conduct several experiments to measure the privacy-preserving effect and usability of our scheme with some closely related schemes. Experimental results on two datasets show the utility and privacy of our scheme and further illustrate its advantages.

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.

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.

With wide development of various information technologies, our daily activities are becoming deeply dependent on cyberspace. People often use handheld devices (e.g., mobile phones or laptops) to publish social messages, facilitate remote e-health diagnosis, or monitor a variety of surveillance. However, security insurance for these activities remains as a significant challenge. Representation of security purposes and their enforcement are two main issues in security of cyberspace. To address these challenging issues, we propose a cyberspace-oriented access control model (CoAC) for cyberspace whose typical usage scenario is as follows. Users leverage devices via network of networks to access sensitive objects with temporal and spatial limitations. We generalize subjects and objects in cyberspace and propose scene-based access control. To enforce security purposes, we argue that all operations on information in cyberspace are combinations of atomic operations. If every single atomic operation is secure, then the cyberspace is secure. Taking applications in the browser-server architecture as an example, we present seven atomic operations for these applications. A number of cases demonstrate that operations in these applications are combinations of introduced atomic operations. We also design a series of security policies for each atomic operation. Finally, we demonstrate both feasibility and flexibility of our CoAC model by examples.

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.

Nowadays, the Space Information Network (SIN) has been widely used in real life because of its advantages of communicating anywhere at any time. This feature is leading to a new trend that traditional wireless users are willing to roam to SIN to obtain a better service. However, the features of exposed links and higher signal latency in SIN make it difficult to design a secure and fast roaming authentication scheme for this new trend. Although some existing researches have been focused on designing secure authentication protocols for SIN or providing roaming authentication protocols for traditional wireless networks, these schemes cannot provide adequate requirements for the roaming communication in SIN and bring in critical issues, such as the privacy leakage or intolerable authentication delay. Observing these problems have not been well addressed, we design an anonymous and fast roaming authentication scheme for SIN. In our scheme, we utilize the group signature to provide the anonymity for roaming users, and assume that the satellites have limited computing capacity and make them have the defined authentication function to avoid the real-time involvement of the home network control center when authenticating the roaming users. The results of security and performance analysis show that the proposed scheme can provide the required security features, while providing a small authentication delay.

Location privacy continues to attract significant attentions from both industry and academia in recent years. However, Location Based Service (LBS) servers or some other adversaries who can monitor a particular user's historical and current status in a long-term way may likely infer user's location privacy. To solve this problem, we propose a Longterm Observation-aware Dummy Selection (LODS) algorithm to achieve k-anonymity for users in LBSs. Different from existing approaches, the LODS takes the historical anonymity sets into account, since mobile users may query LBSs at certain places such as home or office. LODS selects candidate sets containing dummy locations with less number of occurrences firstly, in order to achieve the preferred distribution. Then, LODS further filters out candidate sets with smaller entropy. Finally, we choose the anonymity set with highest Quality of Service (QoS) as the result. Extensive experiment indicates our algorithm can protect user's location privacy effectively against long-term observation, and satisfy user's QoS requirement at the same time.

Effectively collecting real-time data is a fundamental problem in IoT (Internet of Things) searches. In the IoT, most data are linked with the owner's private information and cannot be publicly released on the Internet. This invalidates the use of crawlers to collect data in IoT searches. As a result, effectively motivating potential data providers (PDPs) to provide real-time on demand data becomes a key requirement for the development of an IoT search service. To address this problem, we acknowledge the realistic assumption of incomplete information, and propose a buyout-auction framework, with the constraint of QoD (Quality of Data), to collect real-time data and maximize bidders' payoff. Simulation results demonstrate that our approach can drive PDPs to participate in bidding in a timely manner and provide data under the constraints of QoD to IoT search service providers.

Countermeasure selection is a key process of the Intrusion Response System (IRS). Many cost-sensitive schemes have been proposed to select the optimal countermeasure to maximize security utility by attuning attack damage and response cost. However, existing schemes ignore the interaction between different countermeasures for different attack paths, and neglect the uncertainty between alerts and attacks, which may lead to excessive or insufficient responses. ignore the interaction between different countermeasures for multiple attack paths. To address this problem, in this paper, we propose a combined countermeasures selection scheme based on probabilistic attack tree (PAT). First, we employ Bayesian networks to calculate the probability of each atomic attack in the PAT. Next, the exploitation probability of each attack path is evaluated and multiple possible attack paths are identified. In addition, we quantify the damage of each identified attack path and formulate the countermeasure selection for single attack path as a multi-objective optimization problem. Finally, by considering the security utilities of the countermeasures for different attack paths, we use a greedy strategy to select the combined countermeasures and maximize overall security utility. The experimental results demonstrate the effectiveness of the proposed scheme.

Customized-bus Sharing Service (CSS) enables a centralized server to schedule comfortable bus trips for users by ride clustering based on the individual requirements. It has been increasingly popular in crowded metropolises, bringing a lot of convenience and reducing trip costs to users. Ride clustering is essential for the server to determine the stops of a customized bus, but it also leads to the exposure of users' current locations and spatio-temporal patterns. Although privacy-preserving ride clustering can generate optimal bus routes, it depends on frequent interactions between users and the server, so all the users should be always online. In this paper, we propose a privacy-preserving ride clustering scheme for CSS to support off-line users, in which fog computing is introduced to assist the server in generating bus route without the exposure of users' travel plans. Fog servers are able to perform ride clustering interacting with the server, after receiving the preferred pick-up and drop-off positions from users. Thus, the users are unnecessary to be always online. In addition, the Paillier cryptosystem and randomization technique are leveraged to protect the user's privacy without sacrificing the clustering quality. Finally, the proposed privacy-preserving ride clustering scheme is demonstrated to have the advantage of low computational and communication overhead with high security guarantees.

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.

Security measurement matters to every stakeholder in network security. It provides security practitioners the exact security awareness. However, most of the works are not applicable to the unknown threat. What is more, existing efforts on security metric mainly focus on the ease of certain attack from a theoretical point of view, ignoring the “likelihood of exploitation.” To help administrator have a better understanding, we analyze the behavior of attackers who exploit the zero-day vulnerabilities and predict their attack timing. Based on the prediction, we propose a method of security measurement. In detail, we compute the optimal attack timing from the perspective of attacker, using a long-term game to estimate the risk of being found and then choose the optimal timing based on the risk and profit. We design a learning strategy to model the information sharing mechanism among multiattackers and use spatial structure to model the long-term process. After calculating the Nash equilibrium for each subgame, we consider the likelihood of being attacked for each node as the security metric result. The experiment results show the efficiency of our approach.

Millions of the co-owned items, such as photos, comments etc., are uploaded to OSNs everyday. These co-owned items contain plenty of privacy information. One important information is the social relations which can be inferred from the items. When sharing these items, the owners may just want to make themselves visible alone while hiding these social relations. It leaves us a problem that making each single co-owner visible alone is acceptable, but showing them together is not allowed. To avoid the privacy leakage, we should choose proper owners to be visible to the visitor. Unfortunately, traditional access control for OSNs cannot fit this concern thus cannot provide a suitable answer. To deal with this problem, we first define the `dislike relation' and its corresponding conflicts. Then we propose a communication-intensity-based scheme to measure the social intimacies between the visitor and co-owners of the accessed item. Based on the social intimacies, we can provide a decision support when conflicts occur. Case studies and user studies are performed to illustrate the effectiveness of our proposed scheme.

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.

Friend discovery has been one of the hot topics in our social activities over the past decade. Mobile users have more opportunities to discover and make new social interactions with others in vicinity to build and extend their social communities. However, the inevitable information releasing conflicts with the increasing privacy concerns. In this paper, we employ the concept of friend-of-friend and design a secure and accurate friend discovery for privacy-aware mobile users in Proximity-based Mobile Social Networks (PMSNs). We first construct a novel similarity function with fully considering the number of common attributes, the corresponding priorities and the ratio of matched attributes over all the inputs. Then we develop a secure friend recommendation phase based on a carefully combination of the commutative encryption function and the bilinear pairings. The security and performance are thoroughly analyzed and evaluated via detailed simulations.

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.

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.

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.

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.

Recently, code construction approaching the rate-distortion bound of reversible data hiding has been proposed by Lin , in which the coding/decoding process needs the optimal probability distribution of marked-signals as parameters. Therefore, the efficiency and accuracy of estimating the optimal marked-signal distribution will greatly influence the speeds of encoding and decoding. In this paper, we propose a fast algorithm to solve the optimal marked-signal distribution. Furthermore, we modify the method to achieve the optimal distribution directly according to a given distortion constraint or an expected embedding rate, which makes it more practical for applications.

Recently, more and more attention is paid to reversible data hiding (RDH) in encrypted images, since it maintains the excellent property that the original cover can be losslessly recovered after embedded data is extracted while protecting the image content's confidentiality. All previous methods embed data by reversibly vacating room from the encrypted images, which may be subject to some errors on data extraction and/or image restoration. In this paper, we propose a novel method by reserving room before encryption with a traditional RDH algorithm, and thus it is easy for the data hider to reversibly embed data in the encrypted image. The proposed method can achieve real reversibility, that is, data extraction and image recovery are free of any error. Experiments show that this novel method can embed more than 10 times as large payloads for the same image quality as the previous methods, such as for PSNR=40 dB.

Web services over the Internet are widely used nowadays. The problem of secure access to Web-based systems is of great importance naturally. Compared with the existing models, the action-based access control (ABAC) model is the most suitable to control the access on Web services. In this paper, the ABAC model is introduced. Then, the security architecture of ABAC for Web services is proposed. In the architecture, the action server manages the action information, the domain server determines the security rank of request resources, and the resource server storing the resources with different security ranks responses the request from the user. The cookie is extended with security properties.