The proliferation of Internet of Things (IoT) devices has led to the generation of vast amounts of sensitive and decentralized data. Federated Learning (FL) offers a promising solution by enabling collaborative model training without centralizing raw data. However, existing methods still face challenges such as reduced model accuracy, low robustness, and potential data privacy leakage. To address these issues, we propose a Secure and Robust Federated Learning framework (SRFL) under a dual-server architecture. Specifically, we use two servers to separate the functions of model aggregation and verification, minimizing the threat of single points of failure. We further construct verification models based on perturbed or encrypted client updates to filter malicious updates and defend against poisoning attacks. Meanwhile, we integrate the Cheon-Kim-Kim-Song (CKKS) scheme with random perturbation and ciphertext transformation mechanisms, ensuring that servers cannot access plaintext client updates throughout the training process. Formal security analysis and extensive experiments demonstrate that our scheme consistently achieves high accuracy and robustness under adversarial conditions while preserving data privacy.

In the Internet of Things (IoT) environment, a large number of edge devices collaboratively generate and process data, making Federated Learning (FL) an appealing solution for privacy-preserving model training without sharing raw data. However, traditional FL frameworks suffer from performance degradation due to model poisoning attacks under Non-Independent and Identically Distributed (Non-IID) settings, as well as lack decision-making explainability. To address these challenges, we propose the Robustness-enhanced and explainable Federated Learning (ReFed) framework by combining Local Interpretable Model-agnostic Explanations (LIME) with similarity-based aggregation. Specifically, by fitting a local linear regression model to approximate the decision processes of both the global and local models for improved transparency, and dynamically computing client aggregation weights based on inter-model prediction similarity for enhanced robustness. Experiments demonstrate that ReFed achieves 99.76% accuracy on MNIST and 87.65% accuracy on CIFAR-10, outperforming typical FL methods in adversarial settings.

The proliferation of model-sharing platforms has intensified the need for data-free backdoor detection, as deployed models are often accessed without accompanying clean validation data. This constraint renders traditional, data-dependent detection methods ineffective. However, existing strategies to evade data-free detection are inadequate; they frequently fail to circumvent the multi-faceted discriminative criteria of modern detectors that analyze model output behavior, and they often compromise the backdoor model's primary task performance, thus failing to balance attack stealth with functionality. To evade these detections, this paper introduces a Stealthy Backdoor Attack (SBdA) based on label smoothing. Our method dynamically adjusts the training labels for backdoor samples by leveraging the feature similarity between each class and the attacker's target class. This optimization shapes the backdoored model's output distributions to closely mimic those of a benign model, thereby evading detection mechanisms that rely on outlier characterization and posterior distribution analysis. Extensive experiments demonstrate that SBdA maintains a high attack success rate (exceeding 91% under all tested conditions, with 75% of models surpassing 96%) while significantly reducing its detectability. On CIFAR-10, the average outlier score for our models was 0.554-merely 0.050 higher than benign models-compared to a 25.517 deviation for conventional attacks. On GTSRB, SBdA reduced the outlier score gap by 82.86% compared to the average-label technique. Furthermore, by carefully calibrating the posterior distribution, SBdA effectively avoids detection by posterior matrix-based methods across all four tested datasets.

Data deduplication contributes to avoiding great waste of cloud storage resources. Most of current data deduplication schemes focus on file-level data deduplication, without realizing deduplication ownership privacy protection, data blocks retrieval or error data blocks recovery. To this end, this paper proposes cross-clouds encrypted data fine-grained deduplication and recovery (CC-FGDR) scheme supporting blockchain collaborative computing. CC-FGDR exploits two-party joint generation of deduplication tags to prevent deduplication privacy leakage, and constructs vector-commitment ciphertext tree to realize proofs of ownership verification of distributed backup data blocks via blockchain systems. CC-FGDR achieves secure cross-clouds deduplication detection by constructing twin bloom filter tree and privacy puncture pseudo-random function. To achieve data availability, a ciphertext reconstruction matrix is introduced and combined with countable bloom filter to achieve the location, search and retrieval of distributed outsourced data blocks. CC-FGDR further designs low-density parity check code to achieve cross-clouds encrypted error data recovery through collaborative computing of smart contracts. The security analysis demonstrates CC-FGDR ensures data storage verifiability, data confidentiality, and ownership privacy protection. Performance evaluation demonstrates the feasibility of CC-FGDR in the deployment of multiple clouds via blockchain systems, and CC-FGDR significantly improves the deduplication efficiency by joint deduplication in multi-clouds.

To ensure the legitimacy of communicators while ad dressing the privacy concerns of vehicles in vehicular ad-hoc networks (VANETs), conditional privacy-preserving authentication (CPPA) schemes have been proposed. Given that existing schemes suffer from single point of failure due to centralized authorities, several distributed CPPA schemes have been proposed. However, these schemes all ignore the tight cementation between system secret keys and the authority, which could be a serious threat to system security, that the compromised authority may leak the system secret key. To address these issues, we propose a security enhanced decentralized conditional privacy-preserving authentication (DCPPA) scheme. DCPPA first introduces a decentralized system master key generation (DSMKG) mechanism without a centralized secret sharer, ensuring that the system secret key remains hidden from any single authority. Based on DSMKG, DCPPA then implements a lightweight verifiable pseudonym self-generation strategy without the system master secret key escrow problem, thus providing flexible pseudonym updating and reliable de-anonymization. Moreover, we implement DCPPA over a hyperelliptic curve cryptosystem (HECC) to balance the system performance. Considering the additional communication processes due to the decentralized feature, we introduce a symmetric balanced incomplete block design (SBIBD) to enhance the communication efficiency. We demonstrate the excellent security of DCPPA through an in-depth security analysis, while demonstrate that the overhead of DCPPA is at ms level through experiments.

The proliferation of Vehicular Ad-hoc Networks (VANETs) has brought new security challenges, as malicious vehicles may disseminate fake messages, thereby threatening road safety. Therefore, it is crucial to ensure the credibility of emergency messages while enabling their rapid dissemination. Cloud computing can enhance the efficiency of VANETs, thus facilitating the application of cloud-assisted security mechanisms. Therefore, this paper proposes a Cloud-assisted Multi-dimensional Reputation Management (CMRM) scheme with privacy preservation for emergency message dissemination in VANETs. Specifically, vehicles are represented by multi-dimensional reputation vectors together with dynamically updated multi-dimensional threshold vectors, which enable more fine-grained and reliable reputation evaluation. A confusing process is employed to conceal vehicle identities, reputation vectors, and threshold vectors, thereby ensuring privacy preservation. Furthermore, we design a Reputation Judgment Vector Extraction (RJVE) algorithm. This algorithm, through the collaboration of two cloud servers, can obtain the reputation judgment vector without disclosing sensitive data. Based on this, receivers use the Credible Message Judgment (CMJ) algorithm to efficiently evaluate the credibility of emergency messages. Theoretical analysis demonstrates that the CMRM scheme achieves privacy preservation and resists common attacks with low computation and communication overhead on both the Trusted Authority (TA) and vehicle sides. Simulation results further verify the robustness and efficiency of the CMRM scheme. The probability that a true message is trusted can quickly converge to over 80%, and the probability that a fake message is trusted quickly drops to near 0.

Vehicle platoon, where multiple vehicles travel in formation under a leading vehicle, enhances road capacity and reduces energy consumption. Assessing the reliability of the leading vehicle through reputation management is critical for the vehicle platoon. In the domain of reputation management, reputation updating is of paramount importance. However, many existing schemes are limited by security vulnerabilities, privacy concerns, and reliance on simplistic strategies (i.e., single reputation updating and single-dimensional evaluation), with this reliance undermining their robustness. To address these issues, this paper proposes a Privacy-Preserving Dual Reputation Updating Scheme with Multi-Dimensional Feedback Scores in Vehicle Platoon (PDRU). The PDRU scheme employs a dual reputation model, which enhances the robustness of the reputation updating and provides resilience against the single reputation attack. Meanwhile, it introduces a multi-dimensional evaluation frame work that enables following vehicles to assess the leading vehicle across multiple dimensions, with feedback scores aggregated through weighted averaging according to the importance of each dimension. Moreover, it preserves the vehicle identity privacy, feedback score privacy, leading vehicle's reputation value privacy, and following vehicle's reputation value privacy. Theoretical analysis and simulation evaluation demonstrate that the PDRU scheme achieves a strong balance between robustness, privacy, security, and efficiency, particularly in reducing computation and communication overheads on the Trusted Authority (TA) side.

The Internet of Vehicles (IoV) generates massive sensitive perception data, typically managed by manufacturer-specific domains. While encryption with domain-specific parameters protects confidentiality, many IoV applications require secure cross-domain data sharing to access complementary information, and expressive keyword search for efficient access. However, existing Attribute-Based Keyword Search (ABKS) schemes are designed for single-domain settings, and thus cannot address heterogeneous key management or provide traceability without a universally trusted authority. To address these issues, we propose TCroS, a traceable cross-domain data sharing scheme that generalizes CP-ABE via proxy re-encryption mechanism, enabling ciphertexts generated in one domain to be securely transformed for authorized requesters in another. To provide traceability, TCroS embeds requester identities into decryption keys using Boneh-Boyen signatures, allowing any party (rather than the universally trusted authority) to trace the source of a leaked key. We further extend TCroS to TCroSS, which incorporates privacy-preserving expressive keyword search supporting Boolean queries, thereby enabling efficient retrieval of authorized data while resisting keyword guessing attacks. Formal security analysis proves that our schemes achieve IND-SCPA and IND-SCKA security. Experimental results demonstrate their practicality, showing that cross-domain sharing can be realized with computation and storage overheads comparable to single-domain setting.

The combination of Deep Learning (DL) and Federated Learning (FL) makes it a popular paradigm to train powerful models securely on large-scale data in a distributed way. However, current solutions face challenges such as significant communication overheads for clients with limited resources, potential privacy risks arising from FL's distributed nature, and the inability to maintain model accuracy without loss under high compression ratios. To solve these issues, we propose a lightweight Communication-efficient and Privacy-preserving FL scheme CPFL by designing Cyclic Segmented Compressive Sensing (CSCS) and using efficient Symmetric Homomorphic Encryption (SHE), which greatly reduces the number of transmitted model weights without sacrificing model accuracy. Formal analysis shows the security of CPFL against known-plaintext attacks and ensures model convergence. Extensive experiments demonstrate that CPFL achieves remarkable model accuracy under more than 200× compression ratio, and even reduces the communication cost by 99.5% compared with previous solutions.

The need for enhanced transaction privacy in decentralized finance (DeFi) is critical. However, existing coin mixing solutions often reveal telltale patterns on the blockchain, exposing users to heuristic analysis. This paper presents DeFiMix, an indistinguishable coin mixing scheme engineered to obscure transaction flows while guaranteeing fairness and security. DeFiMix achieves this through a dual-layer mechanism. First, an off-chain secret handshake protocol enables anonymous negotiation between senders and mixers, effectively breaking the link between transactions and participants. Second, on-chain transactions are structured using time-locks and concurrent signatures to resemble common DeFi activities such as staking and lending, rendering them indistinguishable from ordinary operations. Using security analysis and extensive simulations, we validate DeFiMix’s ability to prevent transaction linkage while remaining practically viable. The results underscore DeFiMix’s strong indistinguishability and fairness, alongside its minimal computational demands, establishing it as a compelling solution for privacy-focused transactions within the DeFi ecosystem.

With the widespread use of encrypted spatial data, many range query schemes emerge to address potential security risks caused by access pattern leakage. However, most existing schemes rely on a dual-server model to hide access patterns and often involve complex spatial relation judgments during range comparisons, leading to low query efficiency. To address these issues, we propose a novel Fast and Access Hidden Range Query (FAHRQ) scheme. First, we introduce an efficient range membership verification technique based on Bloom filters and Lagrange interpolation function, combine homomorphic encryption to ensure the confidentiality of spatial data and the computational flexibility of related operations, and realize the access pattern hidden under single server. Then, we construct an index using R-tree and employ Bloom filters and prefix 0-1 encoding to accelerate the minimum bounding rectangle intersection judgment, enabling secure and efficient range queries over encrypted spatial data while maintaining retrieval accuracy. Finally, we give a formal security analysis to show that our scheme achieves access pattern hidden while protecting data security, and conduct extensive experiments to demonstrate that our scheme improves query efficiency by 5 – 7× compared to existing schemes.

Vehicular Ad Hoc Networks (VANETs) are the cornerstone of intelligent transportation systems and autonomous driving. Vehicle-to-road communication, as one of the core services, faces increasing risks of privacy breaches. Signcryption technology effectively ensures secure information transmission. However, existing signcryption schemes still have deficiencies in terms of transmission robustness and identity privacy protection. To solve these issues, this paper proposes a Robust Identity-based Signcryption scheme (RIBSC) for VANETs. In RIBSC, we first design an area session key distribution mechanism based on Chinese Residual Theorem (CRT), which can dynamically revoke the decryption ability of malicious Roadside Units (RSUs) in real time. Only RSUs approved by Trusted Detection Center (TDC) can obtain a valid session private key by conducting one modular operation. We then utilize the traceable pseudonym mechanism to protect the identity privacy of vehicles and RSUs, which can track their true identities when illegal activities occur. We finally provide a rigorous security proof under the random oracle model, and demonstrate the performance advantages of RIBSC through extensive experiments. More attractively, the session information is fixed at only 148 bytes, regardless of the number of RSUs.

With the rapid growth of mobile and edge computing, federated learning (FL) has emerged as a key technology to enable collaborative model training on mobile devices while preserving user privacy. Secure aggregation is an essential component in FL to protect local gradients and compute the global model, but it is vulnerable to threats from high-latency. When some clients arrive late, the pairwise masks among clients cannot be canceled properly, forcing the server to learn the late clients' masks in order to complete the aggregation. As a result, network uncertainty puts the aggregation process at risk of either service interruption or privacy leakage. While double masking is treated as the most effective solution to achieve both robustness and privacy, its computational and communication costs are prohibitive, especially for resource-constrained mobile devices. To address these challenges, we propose an Efficient Mobile-Cloud Collaborative Aggregation for Federated Learning with Latency Resilience (EFL-LR). We leverage Shamir's secret sharing and a key-homomorphic pseudorandom function to ensure privacy for high-latency clients while reducing computation overheads to$\mathcal {O}(n\log ^{2} n + d)$for clients and$\mathcal {O}(n+d)$for the server. Formal security analysis confirms its latency resilience and privacy guarantees. Experimental results show that EFL-LR achieves 2–$3\times$lower client-side computation cost and accelerates server-side aggregation recovery by at least$10\times$.

The widespread adoption of the Internet of Medical Things (IoMT) has spurred the development of blockchain-based models for medical data sharing, designed to ensure both data trustworthiness and privacy protection. In these models, data owners upload medical data to the blockchain via anonymized transactions, enabling researchers to access and analyze the on-chain data. Existing approaches often utilize coin-mixing techniques to obscure the relationships between transacting parties on the blockchain, thereby enhancing privacy. However, such solutions face challenges related to inefficiency and limited data usability, making them unsuitable for deployment in blockchain-based medical scenarios. To address the issue, we propose Medishuffle, an auditable coin-mixing protocol tailored for IoMT applications. Medishuffle introduces a polynomial-based group signature algorithm to facilitate the shuffling process, leveraging identity anonymity instead of traditional data encryption to reduce computational overhead. Additionally, the traceability feature of group signatures enables auditors to reconstruct the sequence of obfuscated transactions when necessary. Through theoretical analysis, we demonstrate that Medishuffle ensures both the unforgeability and traceability of signatures under the random oracle model in the query process. Using experiments, we show that Medishuffle outperforms existing solutions by offering enhanced functionality without a significant increase in computational overhead, making it a practical and efficient solution for IoMT-based medical data sharing.

With the rapid growth of encrypted image data outsourced to cloud servers, achieving data confidentiality and searchability in cloud-assisted Internet of Things (IoT) environments has become increasingly feasible. However, achieving high efficiency and strong security simultaneously over large-scale encrypted image datasets remains a challenge. To address this, we propose a novel efficient and secure content-based image retrieval scheme in cloud-assisted IoT. Specifically, our scheme leverages lattice-based fully homomorphic encryption and homomorphic comparison techniques, utilizing Cheon-Kim–Kim-Song’s batch processing and single-instruction-multiple-data capabilities. This approach significantly reduces the overhead of fully homomorphic computations, making the query process computational complexity independent of dataset size under certain conditions. Moreover, by integrating private information retrieval technology, the scheme enhances privacy by hiding access patterns of image data. Formal security analysis demonstrates that our scheme achieves indistinguishability against chosen-plaintext attack (IND-CPA), and extensive experiments based on real datasets confirm that our scheme is both practical and efficient for real-world applications.

Cloud storage has become prevalent in Internet of Things (IoT) systems, attributed to its robust storage capabilities and user convenience. However, the cloud-based storage model, which separates data ownership from management, introduces integrity challenges due to the vulnerability of data to tampering. To address the risk of data loss and ensure recoverability, the implementation of multiple replicas is a common strategy. Nonetheless, traditional multireplica auditing schemes are not well suited for IoT environments that employ lightweight devices with limited computational capabilities. In response to the aforementioned challenges, we propose a novel multireplica auditing scheme named CCMR, aimed at alleviating the heavy computation cost on the device side. Our scheme leverages an efficient aggregated multisignature algorithm, offloading computationally intensive tasks associated with data tags from lightweight devices to cloud service providers (CSPs) equipped with advanced computational power. The innovative cross-cloud multireplica hash tree structure, named CC-MHT, facilitates the secure and efficient verification of data block structures and ensures consistency of replicas across multicloud environments. Furthermore, by utilizing blockchain as a public random source, a robust challenge-response protocol is established to guard against potential audit failures that could arise from collusion between the third-party auditor and CSPs. The experimental results indicate the high efficiency of the proposed scheme in terms of computation cost.

With the rapid development of the Internet of Things (IoT), a large amount of multimodal medical data is outsourced to the IoT cloud to reduce local computation and storage costs. To retrieve encrypted multimodal medical data in cloud computing, privacy-preserving cross-modal retrieval (PPCMR) has attracted significant attention. However, existing PPCMR solutions are challenging to meet real-time retrieval requirements for large-scale datasets due to linear index structures and complex encryption algorithms. To solve these issues, we propose a novel PPCMR scheme, named CSCMR, based on canonical correlation analysis (CCA) and symmetric key hidden vector encryption (SHVE). First, we use locality-sensitive hashing and CCA to cluster multimodal medical data according to semantic similarity. Then, we build an index structure for Bloom filter based on the clustering results, achieving constant retrieval complexity. Finally, we encrypt each value in the filter using SHVE algorithm, protecting data privacy while reducing encryption computation overheads. Formal security analysis proves that our scheme can resist selective chosen-plaintext attack. Extensive experiments demonstrate that our scheme is effective and feasible.

In the era of Internet of Things (IoT) and federated learning (FL), where distributed training models are essential, the FL paradigm has come into the spotlight for researchers. However, the inconsistency in the sources of client data and non-independent and identically distributed (Non-IID) heterogeneous characteristics lead to loss in model accuracy. Existing method which attempts to homogenize data distribution among clients based on generative adversarial networks (GANs) incurs high computation overheads on clients in IoT. In this article, we propose a lightweight feature prototype knowledge transfer (FPKT) mechanism. By capturing the essence of data categories, FPKT generates pseudo-features without requiring the original data features, thereby efficiently enhancing model accuracy. We formally prove that FPKT resists chosen plaintext attack (CPA) and experiments demonstrate that our scheme achieves a hundredfold increase in computational efficiency and improves model accuracy by up to 40%.

To address the asynchronous challenges stemming from user resource constraints and intermittent network connections in distributed Internet of Things (IoT) systems, asynchronous federated learning (AFL) has been extensively explored in both academic and industrial domains. However, existing AFL approaches still struggle to effectively tackle the issue of model aggregation information loss due to delayed users, which leads to degraded model accuracy. To alleviate the impact of delayed or unavailable model updates on model aggregation, we propose a novel model update enhancement method to compensate for the loss of model aggregation information. Specifically, we utilize delayed model updates as an update agent for this user and correct the weights of these updates within the delayed rounds threshold to ensure the delayed user’s contribution to the model aggregation. Additionally, we integrate symmetric homomorphic encryption (SHE) into AFL to ensure user privacy while simultaneously minimizing computational overhead. Lastly, we conduct extensive experiments to demonstrate that our scheme improves model accuracy by 6.53% compared to state-of-the-art solutions. Our code is available athttps://github.com/MiaoGroup-XDU/acc-improved-ppafl.

Federated learning (FL) with a distributed trust framework effectively mitigates centralized security risks. However, it remains vulnerable to in-protocol Denial-of-Service attacks, resulting in the malicious server refusing to aggregate the valid gradients or terminating the protocol. Additionally, it is susceptible to collaborative attacks, where compromised servers and clients can bypass gradient verification to inject backdoors. To address those issues, we propose a robust and secure aggregation scheme for FL, which extends the efficient 2-party computation (2PC) to a 3-party computation (3PC) with at most one malicious party, resisting abnormal termination and colluding poisoning attacks. In particular, we skillfully combine the replicated secret sharing with$L_{2}$and$L_{\infty }$defense, ensuring the malformed gradients filtering with a noninteractive setup. Moreover, we integrate the player elimination framework to detect misbehavior and guarantee output delivery. The formal security analysis proves that our scheme maintains malicious security even under the colluding model. Extensive experiments demonstrate that robust and secure aggregation scheme for federated learning is more client-friendly and significantly enhances client efficiency by approximately 4 orders of magnitude compared to state-of-the-art methods.

Ensuring the security of data outsourced to cloud is a prerequisite for the application of Internet of Things (IoT) in actual production. Secure search based on homomorphic encryption can provide high security and require no expensive setup procedure, which can be applied to resource-limited devices in IoT. However, the existing schemes usually have poor search performance and do not consider multiowner setting. To solve these issues, we propose an efficient homomorphic encryption-based secure search scheme in multiowner setting. Specifically, we construct a secure search protocol based on multikey homomorphic encryption, which can be deployed in multiowner setting. Meanwhile, we improve the efficiency of our scheme by optimizing the search algorithm. Formal security analysis proves that our scheme is secure against chosen plaintext attack, and extensive experiments demonstrate that our scheme improves the search efficiency by$1000\times $when compared with state-of-the-art solutions.

Mobile electronic health systems collect a large amount of people’s trajectory data through smart devices (e.g., sensors). Generally, to provide data confidentiality, trajectories are encrypted before being uploaded to cloud servers. Trajectory similarity query has gained widespread attention as a mean to control the spread of infectious diseases. Nonetheless, existing solutions often suffer from low query efficiency and access pattern exposure. To solve these issues, we propose an efficient encrypted trajectory similarity query$(\textsf {TraSQ})$for mobile e-health systems. First, we utilize XZ* index and bijective function to generate the unique trajectory encoding value, thereby reducing storage and query costs over large-scale trajectory datasets. Then, we employ a dual cloud model, secure computing protocols and obfuscation technique to conceal the true trajectory similarity from cloud servers, protecting access pattern. Finally, we formally prove that our scheme achieves privacy protection against chosen plaintext attack (CPA), and conduct extensive experiments to demonstrate that our scheme improves the query efficiency by at least 25.4% when compared with existing solutions.

Collaborative authentication, boasting-enhanced accuracy, robust resilience, and optimized efficiency, holds immense promise for autonomous aerial vehicle (AAV) networks. However, existing collaborative authentication methods overlook both the credibility evaluation and incentives of participating nodes, thereby compromising authentication accuracy and resulting in failures. Furthermore, reliance on trusted decision-fusion institution introduces vulnerabilities and single points of failure. To address these issues, we design a credibility-weighted soft authentication approach specifically for AAV networks, thereby enhancing accuracy by effectively integrating the trustworthiness of collaborating nodes. To further encourage active participation from nodes, we introduce an incentive-based reputation system. Finally, based on the above approaches, we propose a distributed authentication method by leveraging blockchain technology and optimization theory that not only emphasizes security but also optimizes resource selection in AAV networks. Theoretical analysis demonstrates our scheme’s distributed authentication with minimized resource consumption under specified security strength, mitigating single points of failure and fulfilling efficient mutual authentication requirements. Experimental results show a remarkable 78.45% increase in authentication accuracy and a 44.51% reduction in resource consumption compared to advanced solution.

Encrypted databases have been extensively studied with the increasing concern of data privacy in cloud services. For practical efficiency, most encrypted database systems are built under Dynamic Searchable Symmetric Encryption (DSSE) schemes to support fast query and update over encrypted data. However, DSSE schemes allow leakages in their security frameworks, especially access-pattern leakages (i.e., the search results corresponding to queried keywords), which lead to various attacks to infer sensitive information of queries and databases. Existing oblivious-access techniques, such as Oblivious RAM and differential privacy, suffer from excessive communication overhead and loss of query accuracy. In this paper, we propose a new DSSE scheme that enables access-pattern hiding keyword search and update operations. Servers can obliviously query and update databases within only a single communication round. Our building block is based on the Distributed Point Function (DPF), an advanced secret sharing technique that provides provable security guarantees against adversaries with arbitrary background knowledge. Moreover, we devise a novel update protocol that integrates DPF and Somewhat Homomorphic Encryption (SHE) such that servers can obliviously update their local data. We formally analyze the security and implement the prototype. The comprehensive experimental results demonstrate the security and efficiency of our scheme.

With the popularity of social media, mobile devices and the Internet, a large amount of multimodal data (e.g, text, image, audio, video, etc.) is increasingly being outsourced to cloud to save local computing and storage costs. To search through encrypted multimodal data in the cloud, privacy-preserving cross-modal retrieval (PPCMR) techniques have attracted extensive attention. However, most of the existing PPCMR schemes lack the ability to resist quantum attacks and have low search efficiency on large-scale datasets. To solve above problems, we first propose a basic PPCMR scheme FECMR using the enhanced Single-key Function-hiding Inner Product Functional Encryption for Binary strings (SFB-IPFE) and cross-modal hashing technology, which achieves the measurement of similarity over encrypted multimodal data while resisting quantum attacks. Then, we design an efficient index KM-tree utilizing the K-modes clustering algorithm. On this basis, we propose an improved scheme FECMR+, which achieves sub-linear search complexity. Finally, formal security analysis proves that our schemes are secure against quantum attacks, and extensive experiments prove that our schemes are efficient and feasible for practical application.

The rapid development of cloud computing has created more favorable conditions for the practical application of privacy-preserving image retrieval technology. However, most existing schemes fail to fully leverage the potential of cloud computing and still face several challenges, primarily in terms of low retrieval precision, weak security, and reliance on the single-key mechanism. To this end, we propose a privacy-preserving multi key image retrieval in cloud environment, named PPMKIR. First, PPMKIR utilizes a convolutional neural network EfficientNetB7 to extract image feature vectors and employs the mini-batch K Means algorithm for feature clustering. Then, PPMKIR encrypts the obtained clusters using the threshold Paillier cryptosystem and constructs a novel index forest based on the encrypted clusters to aggregate data from different sources. Additionally, PPMKIR introduces a tailored search algorithm for the index forest, significantly accelerating the image retrieval process. Comprehensive security analysis demonstrates that PPMKIR not only protects the privacy of outsourced data and queries but also ensures strong security at all stages of the system. Extensive experiments on real-world image datasets validate that PPMKIR achieves outstanding retrieval precision and efficiency.

Recruiting users in mobile crowdsensing (MCS) can make the platform obtain high-quality data to provide better services. Although the privacy leakage during the process of user recruitment has received a lot of research attention, none of the existing work considers the evaluation of the sensing quality of privacy-preserving data submitted by users, which makes the platform incapable of recruiting users suitably to obtain high-quality sensing data, thereby reducing the reliability of MCS services. To solve this problem, we first propose a sensing quality evaluation method based on the deviation and variance of sensing data. According to it, the platform can obtain the sensing quality of privacy-preserving data for each user during the recruitment. Then we model the user recruitment with a limited budget platform as aCombinatorial Multi-Armed Bandit (CMAB)game to determine the recruited users based on the sensing quality of data obtained by evaluation. Finally, we theoretically prove that our algorithm satisfies differential privacy and the upper bound on theregretof rewards is restricted. Experimental results show that our proposal is superior in various properties, and our method has a 73.67% advantage in accumulated sensing qualities compared with comparison schemes.

Next Point-of-Interest (POI) recommendation plays an important role in various Location-Based Social Networks (LBSNs). It main objective is to predict the user's next interested POI based on previous check-in information. Most existing research treats next POI recommendation as a sequence prediction problem, ignoring collaborative signals from other users as well as security. Instead, a Differential Privacy-based Transformer Framework for Next POI Recommendation (LDPTRec) is proposed, which focuses on dynamic privacy protection, category-specific temporal modeling, and privacy-preserving sequence integration. (1) A privacy trajectory flow graph is constructed by using social-aware edge local differential privacy to protect users behavioral and location privacy. (2) A novel temporal category-aware context embedding algorithm is designed to capture diverse temporal patterns of POI categories. (3) A DP-Transformer algorithm with theoretical privacy guarantees, validated by experiments showing 5.6% accuracy gains and 35.17% lower cold-start latency. Ablation studies validate its components effectiveness, and time-cost experiments confirm its enhanced recommendation efficiency. Overall, LDPTRec effectively balances recommendation security and efficiency while improving accuracy.

To motivate data owners’ (DOs’) trading willingness, the existing incentive mechanisms allow DOs to independently disturb data following data consumer's (DC’s) availability requirement. However, they cannot motivate DOs’ honest disturbance, which is attributed to DOs’ independent disturbance without any supervision. Thus, we implement an incentive mechanism for privacy preserved data trading with verifiable data disturbance where an honest-but-curious disturbance generator (DG) is additionally introduced to supervise DOs’ local disturbance and assist disturbance verification between DOs and DC. Specifically, DG generates the disturbance strategies and secretly distributes to DOs following private information retrieval, guaranteeing DOs's local disturbance's privacy and verifiability with our proposed three-level verification algorithm. Subsequently, we model the trading as a game and disturbance verification results determine the compensation and punishment for trading bilateral utilities following Nash Equilibrium where DOs honestly disturb data. Theoretical analysis shows that DOs are motivated to honestly disturb data and their raw data privacy is preserved. Extensive experiments using the real-world dataset demonstrate that the deviating DOs in our scheme can be verified with a probability of more than 90% and the statistical result accuracy can be improved by more than 80% compared with the existing works.

Perturbation-based privacy-preserving truth discovery requires the Service Provider (SP) to calculate the truthful aggregation result from perturbed data of the Data Sources (DSs), which inevitably damages the aggregation accuracy due to perturbation noise added in the data. Thus, the existing works attempt to relieve the perturbation errors by reducing noise amounts or adjusting aggregation weights of DSs. However, the former sacrifices DSs' privacy preservation and the latter has the limited accuracy recovery performance. Aiming at it, we propose an accuracy-enabling differential privacy-preserving truth discovery consisting of an independence-guaranteed data perturbation module and a progressive-private noise elimination module. Specifically, in the first module, SP generates mass of noises following DS's desired perturbation parameters and DS privately obtains one of noise based on private information retrieval. Meanwhile, to realize the perturbation's traceability, SP preserves the ciphertext of DSs' acquired noises, assisting the following noise elimination. In the second module, SP first removes his preserved DS's encrypted noise from perturbed truth according to homomorphic encryption, and then requires DS to decrypt this cleaned truth. The above two processes are progressively and iteratively implemented until all DSs have been involved. Theoretical analysis shows that our scheme can protect DSs' raw data privacy in both truth discovery process and noise elimination process. Extensive experiments using the real-world dataset demonstrate that our scheme can effectively eliminate more than 90% of the perturbation noise effects on the truth discovery accuracy.

Federated learning (FL) is vulnerable to backdoor attacks due to its distributed nature. Existing unilateral defense mechanisms often fail against persistent attack strategies, primarily due to their limited perspectives. To address the challenge of model misclassification on the server side caused by overlooked model similarity drift, and gradient misjudgment on the client side caused by semantic learning imbalances across classes, this paper proposes a collaborative defense framework for federated learning, termed FL-CDF. FL-CDF establishes an end-to-end defense through a bidirectional client-server collaboration mechanism. Specifically: (1) On the client side, an adversarial perturbation-based malicious neuron detection module is introduced. This module measures neuron activation sensitivity by generating adversarial perturbations, and adaptively prunes backdoor neurons exhibiting high sensitivity. (2) On the server side, a multi-dimensional detection scheme is designed, which integrates neuron localization, adversarial sensitivity, and model parameters. By incorporating client-side feedback on malicious neurons, the server performs robust model aggregation. Theoretical analysis verifies the robustness of FL-CDF, and extensive experiments on public benchmarks demonstrate its effectiveness. In the best-case scenario, FL-CDF improves defense performance by 42.5% compared to current state-of-the-art (SOTA) defense.

Allowing users to assure that their files are reliably stored into multiple replicas is critically important but challenging for secure cloud storage. Recently, Damgård et al. [1] designed the first publicly verifiable proof of replicated storage (abbreviated as PRI-POREP) in the private client setup without the fine-grained timing assumption. However, it relies on an “ideal” invertible random permutations (IRPs), whose construction with the structured domain/range remains open even in the random oracle model. Also, it is computationally inefficient in terms of both replicas generation and file update. To address challenges regarding both practicality and efficiency while guaranteeing the security of PRI-POREP, this paper aims at constructing a new proof of replicated storage scheme without timing assumption, named as μPRI-POREP. μPRI-POREP is secure against server-side deletion of replica blocks and it works efficiently, saving computation cost by orders of magnitude, compared to PRI-POREP. Moreover, we demonstrate that μPRI-POREP can also support efficient dynamic update and can be further applied to secure the RSA-Hourglass schemes. Finally, we evaluate μPRI-POREP with a prototype implementation and exhibit that it can achieve comparable performance compared to PRI-POREP and support efficient file update operation.

Cloud-based outsourced Location-based services significantly impact various aspects of daily life but also raise security concerns. Existing secure retrieval schemes for spatiotemporal data exhibit significant shortcomings regarding dynamic updates; they either compromise privacy through information leakage during updates (lacking forward security) or incur excessively high update costs, hindering practical application. To address these limitations, we first propose a basic filter-based spatio-temporal range query scheme Trinity-I that supports lowcost dynamic updates and automatic expansion. Furthermore, to improve security, reduce storage cost, and false positives, we propose a forward secure and verifiable scheme Trinity-II that simultaneously minimizes storage overhead. Formal security analysis demonstrates that both Trinity-I and Trinity-II achieve Indistinguishability under Selective Chosen-Plaintext Attack (IND-SCPA). Finally, extensive experiments demonstrate that our design Trinity-II significantly reduces storage requirements by 80%, enables data retrieval at the 1 million-record level in just 0.01 seconds, and achieves 10× update efficiency than state-of-art.

With the popularity of mobile devices, spatial-textual trajectory query has been deployed in applications such as trajectory-based navigation and travel route recommendation. Massive trajectory data have been outsourced to cloud servers for storage and sharing such as spatial keyword search. However, existing solutions only support similarity queries in the spatial dimension and still incur high storage and query costs, which cannot scale well in large-scale trajectory data scenarios. To solve the above issues, we first achieve an Efficient Range Query over Encrypted Trajectory Data (ERT) using Douglas-Peucker trajectory compression algorithm, random matrix multiplication, filtering-verification mechanism and polynomial fitting technology. Then, we further propose an enhanced Efficient Spatial Keyword Query over Encrypted Trajectory Data (ESKT) by constructing a unified spatial-textual index structure, which can find relevant trajectories that are within some arbitrary geometric range and contain all query keywords. Finally, we formally prove that our schemes are secure against chosen-plaintext-attack, and conduct extensive experiments to demonstrate that our schemes improve the query efficiency by almost 100× when compared with state-of-the-art solutions.

With the rapid expansion of data volumes in cloud computing, more data owners are opting to outsource their data to cloud service providers to reduce local storage and management costs. However, data outsourcing deprives data owners of direct physical control over their data, increasing the risk of unauthorized access and exposure of sensitive information. To mitigate these risks, various privacy-preserving keyword search schemes with access control have been developed, but many are vulnerable to leakage-abuse attacks due to the exposure of access, search or volume patterns, which can lead to privacy breaches in outsourced data and queries. To solve this problem, we propose an oblivious encrypted keyword search scheme with fine-grained access control, called OEKA. It enables efficient oblivious keyword search over encrypted multi-maps by using the adapted XOR filter and distributed point function, ensuring protection of access, search and volume patterns. Moreover, OEKA enforces role-based access control by using polynomial-based access strategy and keyword-based private information retrieval, allowing access policies of retrieved objects to be detecting without revealing the objects themselves. A formal security analysis verifies the scheme’s robustness, and experimental results demonstrate its practical efficiency.

As a new paradigm of distributed learning, Federated Learning (FL) has been applied in industrial fields, such as intelligent retail, finance and autonomous driving. However, several schemes that aim to attack robust aggregation rules and reducing the model accuracy have been proposed recently. These schemes do not maintain the sign statistics of gradients unchanged during attacks. Therefore, the sign statistics-based scheme SignGuard can resist most existing attacks. To defeat SignGuard and most existing cosine or distance-based aggregation schemes, we propose an enhanced model poisoning attack, ScaleSign. Specifically, ScaleSign uses a scaling attack and a sign modification component to obtain malicious gradients with higher cosine similarity and modify the sign statistics of malicious gradients, respectively. In addition, these two components have the least impact on the magnitudes of gradients. Then, we propose MSGuard, a Multi-Strategy Byzantine-robust scheme based on cosine mechanisms, symbol statistics, and spectral methods. Formal analysis proves that malicious gradients generated by ScaleSign have a closer cosine similarity than honest gradients. Extensive experiments demonstrate that ScaleSign can attack most of the existing Byzantine-robust rules, especially achieving a success rate of up to 98.23% for attacks on SignGuard. MSGuard can defend against most existing attacks including ScaleSign. Specifically, in the face of ScaleSign attack, the accuracy of MSGuard improves by up to 41.78% compared to SignGuard.

With the rapid development of mobile computing and the popularity of mobile devices equipped with GPS technology, massive spatial data have become available. Enterprises upload encrypted spatial data to the mobile cloud to save local storage and computation costs. However, the existing secure Geometric Range Search (GRS) solutions are inefficient in terms of building, updating index structure and querying processes. Moreover, the index structures of existing GRS schemes based on Order Preserving Encryption (OPE) leak location order, which may lead to reconstruction attacks. To solve these issues, we first propose an efficient and secure GRS scheme using Radix-Tree, namely GRSRT-I. Specifically, we construct an index structure based on Radix-tree to achieve efficient search and update, then use homomorphic encryption NTRU to resist chosen-plaintext attack, finally design a dual-server architecture to alleviate the burdens on mobile users caused by multiple rounds of interactions. Furthermore, we propose an enhanced scheme, GRSRT-II, by combining Order-Revealing Encryption and OPE, which greatly improves the search efficiency while slightly reducing the security. We formally prove the security of our proposed schemes, and conduct extensive experiments to demonstrate that GRSRT-I can improve the query efficiency by up to at least 1.5 times when compared with previous solutions and GRSRT-II can achieve a higher level of search efficiency.

As a fundamental service in mobile cloud computing, range query has attracted extensive attention. But the existing secure range query schemes not only leak data privacy but also have low query efficiency. To address those issues, we first design a novel range-matched code to convert the range query into code set matching, which aims to hide the order relationship of outsourced data as well as the index of most significant different bit. Based on the designed range-matched code, we propose aPractical andLess LeakageRangeQuery scheme over encrypted mobile cloud data (PLRQ) by integrating XOR filter and multiset hash function. Security analysis shows that PLRQ achieves semantic security and avoids data privacy leakage. Extensive experiments using real datasets demonstrate that, compared with two state-of-the-art solutions-RngMatch and LSRQ, our proposed PLRQ improves the query efficiency both by 2 orders of magnitude, and reduces the storage cost on Cloud Service Provider by about 79.5% and 73.6% respectively.

Data is the essential fuel for deep neural networks (DNNs), and its quality affects the practical performance of DNNs. In real-world training scenarios, the successful generalization performance of DNNs is severely challenged by noisy samples with incorrect labels. To combat noisy samples in image classification, numerous methods based on sample selection and semi-supervised learning (SSL) have been developed, where sample selection is used to provide the supervision signal for SSL, achieving great success in resisting noisy samples. Due to the necessary warm-up training on noisy datasets and the basic sample selection mechanism, DNNs are still confronted with the challenge of memorizing noisy samples. However, existing methods do not address the memorization of noisy samples by DNNs explicitly, which hinders the generalization performance of DNNs. To alleviate this issue, we present a new approach to combat noisy samples. First, we propose a memorized noise detection method to detect noisy samples that DNNs have already memorized during the training process. Next, we design a noise-excluded sample selection method and a noise-alleviated MixMatch to alleviate the memorization of DNNs to noisy samples. Finally, we integrate our approach with the established method DivideMix, proposing Modified-DivideMix. The experimental results on CIFAR-10, CIFAR-100, and Clothing1M demonstrate the effectiveness of our approach.

Federated learning (FL) has become a popular mode of learning, allowing model training without the need to share data. Unfortunately, it remains vulnerable to privacy leakage and poisoning attacks, which compromise user data security and degrade model quality. Therefore, numerous privacy-preserving frameworks have been proposed, among which mask-based framework has certain advantages in terms of efficiency and functionality. However, it is more susceptible to poisoning attacks from malicious users, and current works lack practical means to detect such attacks within this framework. To overcome this challenge, we present DefendFL, an efficient, privacy-preserving, and poisoning-detectable mask-based FL scheme. We first leverage collinearity mask to protect users' gradient privacy. Then, cosine similarity is utilized to detect masked gradients to identify poisonous gradients. Meanwhile, a verification mechanism is designed to detect the mask, ensuring the mask's validity in aggregation and preventing poisoning attacks by intentionally changing the mask. Finally, we resist poisoning attacks by removing malicious gradients or lowering their weights in aggregation. Through security analysis and experimental evaluation, DefendFL can effectively detect and mitigate poisoning attacks while outperforming existing privacy-preserving detection works in efficiency.

With the popularity of blockchains, low transaction throughput has become a significant bottleneck in applications such as cryptocurrencies. Payment channel networks (PCNs) have received attention as a way to improve throughput. However, due to the difficulty of predicting future transactions for nodes, the transactions are prone to failure when the channel balances do not meet required conditions. It has been shown that increasing buffers (queues) in PCNs can increase the success rate of transactions and throughput. Nevertheless, there is no effective transaction scheduling strategy in buffers when transaction values are flexible and variable. To solve this problem, we first formulate the Scheduling Problem in PCNs (named PSP), and then prove it is NP-hard. We design a neural network solver based on the Sequence to Sequence (Seq2Seq) architecture and train the solver using the reinforcement learning method. With the solver, we first give two scheduling strategies to maximize transaction throughput, and then design a PCN simulator for performance evaluation. Extensive experiments are conducted to show the superiority and various performances of our proposal and illustrate that our proposal can get a significant advantage in terms of the transaction throughput compared to the existing works.

Unmanned Aerial Vehicle (UAV)-assisted IoT networks are receiving a lot of attention in academia and industry. For instance, a UAV can fly and hover over sensors, during which time the sensors simultaneously initiate batch access requests to the UAV. Typically, UAV employs batch authentication to efficiently handle these batch accesses. However, an attacker can initiate illegal requests, causing batch authentication to fail. There are various batch identification algorithms to find illegal requests, enabling legitimate sensors to establish service connections quickly. Existing work wants to choose a suitable one based on the specific attack scenario. However, existing work assumes that the percentage r% of illegal requests is known in advance, which is impractical in real-world scenarios. Besides, existing work only selects a suitable batch identification algorithm based on r%, limiting the performance of batch identification to the capabilities of the alternative algorithms. Drawing inspiration from the Kalman filter, we first propose an adaptive estimation algorithm for the number of illegal requests to address the above problems. Based on the estimated value e%, we design a combinatorial batch identification using reinforcement learning. This approach allows the combination of different algorithms to achieve superior performance. Extensive experiments demonstrate that, for the estimation algorithm, the relative error is less than 20% in 27 out of 40 experiments. Regarding the combinatorial algorithms, the delay can be reduced by approximately 7.15% to 30.86% compared to existing methods.

Secure inference is the main technology to avoid privacy leakage when reasoning with machine learning models. The existing secure inference schemes have been widely explored in the academic field, but when the neural network model is large, existing solutions based on Trusted Execution Environments (TEE) must be configured with a large number of trusted devices due to insufficient hardware memory, which incurs high deployment costs. In addition, existing homomorphic encryption schemes have a high computational cost for ciphertexts, which incurs high time cost when the model is large. To address these issues, we propose a low time overhead secure inference scheme by training a generative adversarial network, which can reduce the calculations for ciphertext. We also use a trusted execution environment to compute nonlinear functions in neural networks, which solves the problem of high computational cost for nonlinear functions in HE while suppressing the growth of noise in HE. Security analysis proves that our scheme achieves semi-honest security, and extensive experiments demonstrate that our scheme has lower time overhead in large neural networks when compare with previous solutions.

GPS-enabled Internet of Things devices, which can obtain the location and temporal information of installed objects to promote location-based service, are completely changing our lives. In recent years, the confidentiality and privacy of personal data have attracted widespread attention, especially when outsourcing to third-party providers. To achieve both the confidentiality and availability of outsourced data, various dynamic searchable symmetric encryption (DSSE) schemes have been proposed. However, existing solutions are limited either in terms of security or efficiency. To address this challenge, we propose a secure and efficient search over encrypted spatiotemporal data (SES-ESTD) scheme that utilizes constrained pseudo-random function and enhanced asymmetric scalar-product preserving encryption. Our scheme not only achieves high retrieval efficiency but also ensures forward security and content privacy. We provide a formal security analysis to prove SES-ESTD is forward secure and content private. Furthermore, extensive experiments indicate that SES-ESTD incurs lower computation and storage overheads compared to other schemes. Most importantly, SES-ESTD achieves millisecond-level retrieval for millions of data points and provides a retrieval speed that is 2.85 times faster than existing forward secure spatiotemporal DSSE schemes.

Image search is a hot topic, which has played a significant role in various Internet of Things (IoT) applications, such as disease diagnosis, face recognition, and fingerprint recognition. Meanwhile, the proliferation of images has led image owners to outsource images to the cloud for reducing local storage and computation burdens. Therefore, image search without compromising privacy over cloud has received considerable attention and extensively explored in the literature. Many Bloom filter (BF)-based schemes have been put forth in past years, however, most of them suffer from high storage overhead, low false positive rate, and even expose the values in BF. To solve these challenges, in this article, we first design a merged and repeated indistinguishable BF (MRIBF) index structure, which can reduce the storage overhead and achieve adaptive security with a low false positive rate. Then, with the MRIBF, we propose a secure and efficient BF-based image search (BFIS) scheme to achieve a faster-than-linear and more accurate search. Detailed theoretical analysis shows that our scheme is really accurate and secure. Extensive experiments demonstrate that our scheme is indeed efficient and feasible.

Searchable encryption (SE) can potentially be used to guarantee both data confidentiality and searchability over mobile cloud-assisted Internet of things. However, existing SE solutions mainly focus on user revocation rather than keyword revocation. The keyword revocation may be required in certain situations. For example, patients do not allow their doctors to access records on some diseases such as syphilis. Hence, we propose a basic Revocable Keyword Search (RKS) scheme over encrypted electronic medical records in the group setting, which supports keyword revocation (by using a revocation list) and authorized access permissions (via a group key exchange protocol). Then, we design an enhanced RKS (called RKS+) to significantly reduce the size of revoked keyword ciphertexts and the costs of token generation and ciphertext retrieval. Our schemes also support efficient user revocation by updating only one index component, and guarantee forward security. The formal security analysis proves that our schemes are secure against both chosen-keyword attacks and chosen-plaintext attacks, and findings from the empirical evaluations demonstrate that our schemes are efficient and practical.

Blockchain-based Industrial Internet of Things (IIoT) integrates the blockchain technology into the traditional IIoT infrastructure to provide secure and collaborative services. In IIoT, the traffic is usually encrypted using a cipher suite (SSL/TLS) for secure communication, which makes it hard for middleboxes (MBs) to detect malicious activity in the traffic. To address this problem, secure MBs that directly perform encrypted traffic inspection have been presented. Recently, a new privacypreserving deep packet inspection (DPI) system on MB for IoT scenarios was proposed, but it suffered from the following two limitations: 1) no support for fast token detection and 2) no support for tracing abnormal sources. To address the two limitations, we propose BTDPI, a privacy-preserving traceable DPI system that efficiently performs inspection over encrypted traffic in blockchain-based IIoT. Technically, we adopt a two-layer filter architecture to improve the efficiency of detection and moreover introduce a new online–offline certificateless aggregate signature with smart contract to design an identity traceability mechanism. The experiment result shows that BTDPI runs$26.7\times $faster for token detection with 3000 tokens and 3000 rules than the state-of-the-art work.

Deep Convolution Neural Networks (CNNs) have become the cornerstone of image classification, but the emergence of adversarial image attacks brings serious security risks to CNN-based applications. As a local perturbation attack, the border attack can achieve high success rates by only modifying the pixels around the border of an image, which is a novel attack perspective. However, existing border attacks have shortcomings in stealthiness and are easily detected. In this article, we propose a novel stealthy border attack method based on deep feature alignment. Specifically, we propose a deep feature alignment algorithm based on style transfer to guarantee the stealthiness of adversarial borders. The algorithm takes the deep feature difference between the adversarial and the original borders as the stealthiness loss and thus ensures good stealthiness of the generated adversarial images. To ensure high attack success rates simultaneously, we apply cross entropy to design the targeted attack loss and use margin loss as well as Leaky ReLU to design the untargeted attack loss. Experiments show that the structural similarity between the generated adversarial images and the original images is 8.8% higher than the state-of-art border attack method, indicating that our proposed adversarial images have better stealthiness. At the same time, the success rate of our attack in the face of defense methods is much higher, which is about four times that of the state-of-art border attack under the adversarial training defense.

Keyword-based search over encrypted data is an important technique to achieve both data confidentiality and utilization in cloud outsourcing services. While commonly used access control mechanisms, such as identity-based encryption and attribute-based encryption, do not generally scale well for hierarchical access permissions. To solve this problem, we propose a Role-based Encrypted Keyword Search (REKS) scheme by using the role-based access control and broadcast encryption. Specifically, REKS allows owners to deploy hierarchical access control by allowing users with parent roles to have access permissions from child roles. Using REKS, we further facilitate token generation preprocessing and efficient user management, thereby significantly reducing the users' final token generation and index update overheads, respectively. Formal security analysis proves that REKS is secure against chosen keyword and internal keyword guessing attacks, and findings from the empirical evaluations demonstrate that REKS is efficient and practical.

Federated Learning (FL) ensures collaborative learning among multiple clients while maintaining data locally. However, the traditional synchronous FL solutions have lower accuracy and require more communication time in scenarios where most devices drop out during learning. Therefore, we propose anAsynchronousFederatedLearning (AsyFL) scheme using time-weighted and stale model aggregation, which effectively solves the problem of poor model performance due to the heterogeneity of devices. Then, we integrate Symmetric Homomorphic Encryption (SHE) into AsyFL to proposeAsynchronousPrivacy-PreservingFederatedLearning (Asy-PPFL), which protects the privacy of clients and achieves lightweight computing. Privacy analysis shows that Asy-PPFL is indistinguishable under Known Plaintext Attack (KPA) and convergence analysis proves the effectiveness of our schemes. A large number of experiments show that AsyFL and Asy-PPFL can achieve the highest accuracy of 58.40% and 58.26% on Cifar-10 dataset when most clients (i.e., 80%) are offline or delayed, respectively.

The cloud-assisted mobile electronic health (e-health) system facilitates e-health data sharing between healthcare providers and patients, but also raises the security and privacy concerns of e-health data. Although Ciphertext-Policy Attribute-Based Encryption (CP-ABE) has been a promising technique to achieve fine-grained access control over encrypted e-health data, it still incurs high encryption and decryption burdens on mobile users such as smartphones and sensors. In addition, malicious cloud servers may conduct incorrect operations due to various interest incentives (e.g., leaking sensitive information to illegal users, saving computation and storage costs). To solve the above issues, in this paper we first propose an Outsourced CP-ABE (OABE) with verifiable encryption scheme by splitting secret keys corresponding to an attribute set and using the short signature, which not only reduces the encryption and decryption complexities of mobile users but also guarantees that cloud servers correctly perform encryption operations. Then, we extend OABE to construct outsourced CP-ABE with verifiable decryption (OABE+) by utilizing the verifiable tag mechanism, which guarantees that cloud servers correctly conduct the ciphertext transformation. Formal security analysis proves that our schemes are selectively secure against unauthorized accesses and malicious operations. Extensive experiments using various real-world datasets demonstrate that our schemes are efficient and feasible in real applications.

Due to the powerful representation ability and superior performance of Deep Neural Networks (DNN), Federated Learning (FL) based on DNN has attracted much attention from both academic and industrial fields. However, its transmitted plaintext data causes privacy disclosure. FL based on Local Differential Privacy (LDP) solutions can provide privacy protection to a certain extent, but these solutions still cannot achieve adaptive perturbation in DNN model. In addition, this kind of schemes cause high communication overheads due to the curse of dimensionality of DNN, and are naturally vulnerable to backdoor attacks due to the inherent distributed characteristic. To solve these issues, we propose anEfficient andSecureFederatedLearning scheme (ESFL) against backdoor attacks by using adaptive LDP and compressive sensing. Formal security analysis proves that ESFL satisfies$\epsilon$-LDP security. Extensive experiments using three datasets demonstrate that ESFL can solve the problems of traditional LDP-based FL schemes without a loss of model accuracy and efficiently resist the backdoor attacks.

To motivate data owner (DO) to trade data, the existing data trading allows DO to sell the disturbed data to the data consumer (DC), where the disturbance parameter and the data price are negotiated by them, and DO independently adds the disturbance noise to data (usually continuous type) following the negotiation result. However, DOs may violate the negotiated parameter and add more noise to data while obtaining the negotiated price, which damages DC's disturbed data availability. This deficiency is rooted in the absence of supervision and verifiability on DOs' independent disturbances. Aiming at the above problem, we devise a privacy-preserved data trading via verifiable data disturbance. Specifically, the honest-but-curious disturbance server (DS) is introduced to generate encrypted verifiable disturbance noises, and secretly distribute noises to DOs referring to the method of private information retrieval. Using homomorphic encryption, DOs finish data disturbance without knowing noises' specific sizes. Subsequently, DC selects DOs to verify with our proposed anti-forgery verification, where the anti-forgery on both disturbance noise and original data guarantees verification correctness. Theoretical analysis proves that DOs' original data is preserved in data trading. Extensive experiments using the real-world dataset demonstrate that our scheme can detect more than 80% of malicious DOs and decrease their utilities to punish malicious disturbance compared with existing works.

Federated Learning (FL) is an emerging distributed machine learning paradigm crucial for ensuring privacy-preserving learning. In FL, a fair incentive mechanism is indispensable for inspiring more clients to participate in FL training. Nevertheless, achieving a fair incentive mechanism in FL is an arduous endeavor, underscored by two significant challenges that persistently elude resolution within existing methodologies. Firstly, existing works overlook the issue of category distribution heterogeneity in contribution evaluation, leading to incomplete contribution evaluations. Secondly, the fact that malicious servers will dishonestly allocate rewards to save costs is not considered in existing work, which can be a barrier to client participation in FL. This paper introduces FDFL (Fair andDiscrepancy-aware incentive mechanism forFederatedLearning), a novel system addressing these concerns. FDFL encompasses two key elements: 1) Discrepancy-aware contribution evaluation approach; 2) Provable reward allocation approach. Extensive experiments on four model-dataset combinations demonstrate that, under the heterogeneous setting, our scheme improves accuracy by an average of 9.85% and 11.97% compared to FedAvg and FAIR, respectively.

Privacy-preserving range query, which allows the server to implement secure and efficient range query on encrypted data, has been widely studied in recent years. Existing privacy-preserving range query schemes can realize effective range query, but usually suffer from the low efficiency and security. In order to solve the above issues, we propose an Efficient and Privacy-preserving encode-based Range Query over encrypted cloud data (namely basic EPRQ), which encodes the data and range by using Range Encode (REncoder), and then encrypts the codes via Additional Symmetric-Key Hidden Vector Encryption (ASHVE) technology. The basic EPRQ can achieve effective range query while ensuring privacy protection. Then, we split the codes to reduce the storage cost. We further propose an improved scheme, EPRQ+, which constructs a binary tree-based index to achieve faster-than-linear retrieval. Finally, our formal security analysis proves that our schemes are secure against Indistinguishability under Chosen-Plaintext Attack (IND-CPA), and extensive experiments demonstrate that our schemes are feasible in practice, where EPRQ+ scheme improves the storage efficiency by about 4 times and the query efficiency by about 8 times compared to the basic EPRQ.

To address the challenges posed by data silos and heterogeneity in distributed machine learning, privacy-preserving asynchronous Federated Learning (FL) has been extensively explored in academic and industrial fields. However, existing privacy-preserving asynchronous FL schemes still suffer from the problem of low model accuracy caused by inconsistency between delayed model updates and current model updates, and even cannot adapt well to Non-Independent and Identically Distributed (Non-IID) settings. To address these issues, we propose a Privacy-preserving Asynchronous Federated Learning based on the alternating direction multiplier method (PAFed), which is able to achieve high-accuracy models in Non-IID settings. Specifically, we utilize vector projection techniques to correct the inconsistency between delayed model updates and current model updates, thereby reducing the impact of delayed model updates on the aggregation of current model updates. Additionally, we employ an optimization method based on alternating direction multipliers to adapt the Non-IID settings to further enhance the global model accuracy. Finally, through extensive experiments, we demonstrate that our scheme improves the model accuracy by up to 12.53% when compared with current state-of-the-art solution FedADMM.

Federated learning not only realizes collaborative training of models, but also effectively maintains user privacy. However, with the widespread application of privacy-preserving federated learning, poisoning attacks threaten the model utility. Existing defense schemes suffer from a series of problems, including low accuracy, low robustness and reliance on strong assumptions, which limit the practicability of federated learning. To solve these problems, we propose a Robustness-enhanced privacy-preserving Federated learning with scaled dot-product attention (RFed) under dual-server model. Specifically, we design a highly robust defense mechanism that uses a dual-server model instead of traditional single-server model to significantly improve model accuracy and completely eliminate the reliance on strong assumptions. Formal security analysis proves that our scheme achieves convergence and provides privacy protection, and extensive experiments demonstrate that our scheme reduces high computational overhead while guaranteeing privacy preservation and model accuracy, and ensures that the failure rate of poisoning attacks is higher than 96%.

Boolean range query (BRQ) as a typical type of spatial keyword query that is widely used in geographic information systems, location-based services and other applications. It retrieves the objects inside the query range and containing all query keywords. Many privacy-preserving BRQ schemes have been proposed to support BRQ over encrypted data. However, most of them fail to achieve efficient retrieval and lightweight result verification while suppressing access and search pattern leakage. Thus, in this paper, we propose an efficient verifiable privacy-preserving Boolean range query with suppressed leakage. Firstly, we convert BRQ into multi-keyword query by using Gray code and Bloom filter. Then, we achieve efficient oblivious multi-keyword query by combining distributed point function and PRP-based Cuckoo hashing, which protects the access and search patterns. Moreover, we support lightweight and oblivious result verification based on oblivious query, aggregate MAC, keyed-hashing MAC and XOR-homomorphic pseudorandom function. It enables query users to verify the result integrity with a proof whose size is independent of the size of the outsourced dataset. Finally, formal security analysis and extensive experiments demonstrate that our proposed scheme is adaptively secure and efficient for practical applications, respectively.

Cloud computing has been a research focus in both academic and industrial communities for decades. Along with this trend, Searchable Encryption (SE) technology emerged and developed as data privacy concerns increased. Many schemes are proposed to solve the privacy-preserving data-sharing problem in multi-user scenarios. Most existing solutions are based on the assumption that all users are trusted. However, there will be cross-user leakage when there are malicious or compromised ones. This is because of the inherent linkability of authorization information and the search result when multiple users request data from the same database. To this end, we propose a cross-user leakage mitigation scheme for authorized encrypted data sharing in a two-server model. We utilize a blinding factor to delink authorizations based on Symmetric Multi-Key Searchable Encryption (SMKSE). To break the linkability of query results, we combine the zero-sum garbled Bloom filter with the oblivious transfer technique, where each of the two servers can only know partial information. We devise a group-based Bloom filter structure in indices to improve efficiency. We perform formal security analysis and also demonstrate the efficiency through comparative experiments.

The advanced data trading allows data generator’s (DG) disturbed data to be traded as both initial and reselling trading modes, which meets DG’s raw data privacy and data consumers’ (DCs) vast data requirement. However, the traded data truthfulness verifiability cannot be guaranteed in the privacy-preserved way. Firstly, due to DG’s independent and random disturbance, DC cannot verify whether the traded data is disturbed under his required disturbance parameter without carrying privacy leakage on DG. Secondly, because the reselling trading is allowed, DC can hardly verify the traded data’s origin truthfulness under the deceiving of data reseller (DR) while protecting his purchase privacy. Aiming at the above problems, we propose the privacy-preserved data disturbance and truthfulness verification for data trading. Specifically, an honest-but-curious trading server (TS) is introduced to assist our devised private-verifiable imprint-embedded disturbance method where imprint is blinding. Subsequently, TS implements the adaptive truthfulness verification by constructing imprint-embedded individual verification formula and requiring verified participants to decrypt the formula result. The verified participants cannot inform the blinding imprint value to forge the correct result, ensuring the accuracy of the devised verification method. Theoretical analysis proves that participants’ privacy is preserved and the traded data’s truthfulness can be guaranteed. Extensive experiments using the real-world dataset demonstrate that without any extra privacy cost, our scheme verifies 100% untruthful traded data compared with the existing solutions’ 50%.

Secure inference is the main technology to avoid privacy leakage when reasoning with machine learning models in multiparty settings for industrial Internet of Things (IoT). Existing solutions based on secure multiparty computation have been extensively explored in both academic and industrial fields, but these solutions are deployed in single user single model provider setting. When multiparty collaborate to handle tasks in multiuser multimodel provider setting, these solutions are no longer applicable as the mechanism of the linear computation protocol restricting the number of parties increases. In addition, these existing schemes need to conduct multiple checks during the inference of malicious models, which results in additional communication overhead. To solve these issues, we propose a multiparty secure inference scheme by using an improved multiplication protocol for achieving matrix multiplication, which achieves efficient linear calculation and supports adaptive parties. We also design a new check protocol to inspect calculation results of all layers with just one execution, which achieves lower communication and calculation overheads. Formal security analysis proves that our scheme achieves malicious security in the honest-majority setting, and extensive experiments demonstrate that our scheme reduces the communication costs by about 30% in large neural networks when compare with previous solutions.

To solve the data silos issue in distributed machine learning with privacy leakage, privacy-preserving federated learning (PPFL) has been extensively explored in both academic and industrial fields. However, the existing PPFL solutions still suffer from high computation and communication overheads, which result in excessive consumption of communication bandwidth and slow down the training process of FL. To address these issues, we propose a secure and communication-efficient FL scheme using improved compressed sensing and CKKS homomorphic encryption. Specifically, we implement a lossy compression of the model by using discrete cosine transform, then use CKKS homomorphic encryption to encrypt the data transmitted between clients and center server due to its high efficiency and support for batch encryption. Formal security analysis proves that our scheme is secure against indistinguishability under chosen plaintext attack and extensive experiments demonstrate that our scheme achieves a high accuracy at 0.05% compression rate.

Federated Learning (FL) suffers from low convergence and significant accuracy loss due to local biases caused by non-Independent and Identically Distributed (non-IID) data. To enhance the non-IID FL performance, a straightforward idea is to leverage the Generative Adversarial Network (GAN) to mitigate local biases using synthesized samples. Unfortunately, existing GAN-based solutions have inherent limitations, which do not support non-IID data and even compromise user privacy. To tackle the above issues, we propose a GAN-based unbiased FL scheme, calledFlGan, to mitigate local biases using synthesized samples generated by GAN while preserving user-level privacy in the FL setting. Specifically,FlGanfirst presents a federated GAN algorithm using the divide-and-conquer strategy that eliminates the problem of model collapse in non-IID settings. To guarantee user-level privacy,FlGanthen exploits Fully Homomorphic Encryption (FHE) to design the privacy-preserving GAN augmentation method for the unbiased FL. Extensive experiments show thatFlGanachieves unbiased FL with$10\%-60\%$accuracy improvement compared with two state-of-the-art FL baselines (i.e., FedAvg and FedSGD) trained under different non-IID settings. The FHE-based privacy guarantees only cost about 0.53% of the total overhead inFlGan.

With the rapid development of geographic location technology and the explosive growth of data, a large amount of spatial data is outsourced to the cloud server for reducing the local high storage and computing burdens, but at the same time causes security issues. Thus, extensive privacy-preserving spatial data query schemes have been proposed. Most of the existing schemes use Asymmetric Scalar-Product-Preserving Encryption (ASPE) to encrypt data, but ASPE has proven to be insecure against known plaintext attack. And the existing schemes require users to provide more information about query range and thus generate a large amount of ciphertexts, which causes high storage and computational burdens. To solve these issues, based on enhanced ASPE designed in our conference version, we first propose a basic Privacy-preserving Spatial Data Query (PSDQ) scheme by using a new unified index structure, which only requires users to provide less information about query range. Then, we propose an enhanced PSDQ scheme (PSDQ$^+$) by using Geohash-based$R$-tree structure (called$GR$-tree) and efficient pruning strategy, which greatly reduces the query time. Formal security analysis proves that our schemes achieve Indistinguishability under Chosen Plaintext Attack (IND-CPA), and extensive experiments demonstrate that our schemes are efficient in practice.

To motivate users' assistance for protecting others' location privacy by distributedK-anonymity in Location-Based Service (LBS), many incentive mechanisms have been proposed, where users obtain monetary compensation for their assistance. However, most existing distributedK-anonymity incentive mechanisms rely on trusted third parties and ignore users' malicious strategies, which destroys LBS's distributed structure as well as leads to users' privacy leakage and incentive ineffectiveness. To solve the above problems, we propose aPrivacy-Enhanced incentive mechAnism for distributedK-anonymity (PEAK). With determining the monetary transaction relationship and location transmission between users, PEAK enables the anonymous cloaking region construction without the trusted server. Meanwhile, PEAK devises role identification mechanism and accountability mechanism to restrain and punish malicious users, which protects users' location privacy and implements effective motivation on users' assistance. Theoretical analysis based on the game theory shows that PEAK constrains users' malicious strategies while satisfying individual rationality, computational efficiency, and satisfaction ratio. Extensive experiments based on the real-world dataset demonstrate that PEAK improves security and feasibility, especially reaching the success rate of anonymous cloaking region construction to more than 90$\%$and decreasing the malicious users' utilities significantly.

Electronic health (e-health) systems may outsource data such as patient e-health records to mobile cloud servers for efficiency gains (e.g., minimizing local storage and computation costs). However, such a move may result in privacy implications in the presence of semi-honest cloud servers. Searchable Encryption (SE) can potentially facilitate privacy-preserving searches based on keywords for encrypted data stored in the mobile cloud, but most existing SE solutions do not support temporal access control (i.e., a mechanism that grants access permissions to users for specified time ranges). Hence, in this paper we design a time-controllable keyword search scheme by using an attribute-based comparable access control. This allows users to match indexes encrypted at specified time intervals. Then, we improve the basic framework to support efficient user revocation using secret sharing. We then formally prove the security of our proposed frameworks against chosen-keyword attack and key collusion attack, as well as achieving keyword secrecy. We also evaluate the performance of our proposed approach using a real-world dataset to demonstrate their practical utility.

The mobile Internet of Vehicles (IoVs) has great potential for intelligent transportation, and creates spatial data query demands to realize the value of data. Outsourcing spatial data to a cloud server eliminates the need for local computation and storage, but it leads to data security and privacy threats caused by untrusted third-parties. Existing privacy-preserving spatial range query solutions based on Homomorphic Encryption (HE) have been developed to increase security. However, in the single server model, the private key is held by the query user, which incurs high computation and communication burdens on query users due to multiple rounds of interactions. Moreover, exposing data access patterns to semi-honest servers is highly vulnerable to frequency and statistical attacks. To solve these issues, in this paper we propose a secure spatial location query within arbitrary geometric range while protecting access pattern. Specifically, we apply Paillier algorithm and polynomial fitting technique to achieve secure arbitrary geometric range query, design secure and efficient search protocol to hide data access patterns and alleviate query users from high computation and communication burdens under dual-server model. Formal security analysis shows that our scheme is secure under semi-honest model, and extensive experiments demonstrate that our work can reduce users' communication costs by more than 90% compared to previous schemes under single server model, which is practice in real-world scenarios.

UAV-assisted mobile edge computing (UAV-MEC) has been proposed to offer computing resources for smart devices and user equipment. UAV cluster aided MEC rather than one UAV-aided MEC as edge pool is the newest edge computing architecture. Unfortunately, the data packet exchange during edge computing within the UAV cluster hasn't received enough attention. UAVs need to collaborate for the wide implementation of MEC, relying on the gossip-based broadcast protocol. However, gossip has the problem of long propagation delay, where the forwarding probability and neighbors are two factors that are difficult to balance. The existing works improve gossip from only one factor, which cannot select suitable forwarding probability and avoid redundant messages. Besides, these schemes do not consider the historical packet reception of new neighbors when UAVs fly around, which decreases forwarding efficiency. To solve these problems, we first propose a data structure called Bitgraph that can record the historical packet reception of UAVs. Then, we formulate gossip broadcasting as a partially observable Markov decision process. Based on Bitgraph, we design the reward function. Finally, we design a multi-agent reinforcement learning algorithm, Branching Deep Graph Network (BDGN), which simultaneously makes decisions on forwarding probability and neighbors. Extensive experiments illustrate that our proposal gets more than 29% advantage in terms of the propagation delay and 20% advantage in terms of the redundant messages compared to the existing works.

Mobile crowdsensing (MCS) has been applied in various fields to realize data sharing, where multiple platforms and multiple Mobile Users () have appeared recently. However, aiming at mutual selection, the existing works ignore making ’ utilities with the limited resources and platforms’ utilities while achieving the desired sensing data quality maximum as far as possible. Thus, they cannot motivate both and platforms to participate. To address this problem, standing on both sides of and platforms with conflicting interests, we propose a Progressive two-stage Auction-based Multi-platform Multi-user Mutual Selection scheme (). Specifically, in, we treat mutual selection as a two-stage auction and devise the auction models for and platform using forward and reverse auction ideas, presenting and maximizing the utilities from their respective perspectives. Then, based on the proposed progressive two-stage auction structure, we adopt 0-1 knapsack and Myerson’s price theory to construct the first stage -oriented auction and the second stage platform-oriented auction, achieving devised models. Theoretical analysis shows that is economically robust. Extensive experiments on the real dataset demonstrate that respectively promotes platforms’ and ’ utilities by 76.23% and 10.74 times, compared with the existing works.

The utilization of blockchain technology as a distributed information sharing system has gained widespread adoption across various domains. However, its application to Flying Ad-Hoc Network (FANET), characterized by severe packet loss, poses significant challenges. The high packet loss rates in FANETs can result in decreased consensus success rates and negatively impact information sharing consistency and efficiency. In this paper, we proposed RoUBC, a novel consensus scheme for Flying Ad-Hoc Networks (FANET), which is based on the Raft protocol and is designed to address the challenges posed by the severe packet loss network in FANET. The proposed scheme consists of two phases: leader election and block consensus. In the leader election phase, we integrate multi-criteria decision-making and link prediction algorithms to design an efficient stable-leader election method. In the block consensus phase, we propose a dynamic block verification algorithm based on historical verification information to achieve efficient block consensus. Our theoretical analysis demonstrates that the proposed consensus protocol is safe and live, effectively ensuring the consistency of message sharing in FANET. Experiment results show that our scheme outperforms traditional Raft schemes, with 35% increase in consensus success rate and 25% improvement in consensus efficiency.

Pre-trained encoders in computer vision have recently received great attention from both research and industry communities. Among others, a promising paradigm is to utilize self-supervised learning (SSL) to train image encoders with massive unlabeled samples, thereby endowing encoders with the capability to embed abundant knowledge into the feature representations. Backdoor attacks on SSL disrupt the encoder's feature extraction capabilities, causing downstream classifiers to inherit backdoor behavior and leading to misclassification. Existing backdoor defense methods primarily focus on supervised learning scenarios and cannot be effectively migrated to SSL pre-trained encoders. In this article, we present a backdoor defense scheme based on self-supervised knowledge distillation. Our approach aims to eliminate backdoors while preserving the feature extraction capability using the downstream dataset. We incorporate the benefits of contrastive and non-contrastive SSL methods for knowledge distillation, ensuring differentiation between the representations of various classes and the consistency of representations within the same class. Consequently, the extraction capability of pre-trained encoders is preserved. Extensive experiments against multiple attacks demonstrate that the proposed scheme outperforms the state-of-the-art solutions.

With the rapid growth of data size, a large number of data providers outsource their private data to cloud servers to reduce the high storage and computation burdens, but it also leads to security issues such as privacy leakage. Therefore, many privacy-preserving range query schemes have been proposed. However, most of existing secure range query schemes suffer from low query efficiency and expensive computation and update overheads. To address these issues, we propose a novel Fast Range Query (FRQ) scheme for large-scale encrypted Key-Value (KV) data. First, we introduce REMIX, a space-efficient KV index data structure based on Log-Structured Merge-trees (LSM-trees), which maintains a global sorted view of KV pairs across multiple table files for efficient range queries. Besides, we exploit the write-efficiency compression strategy of LSM-trees to ensure efficient dynamic data updates. Finally, we use Czech Havas Majewski (CHM) to protect the index structure, which reduces the computation overhead and ensures the retrieval accuracy. Formal security analysis proves that our scheme can achieve an acceptable level of security. Extensive experiments demonstrate that our scheme improves the query efficiency by nearly$8\times$and update efficiency by$7\times$compared to state-of-the-art solutions over million-level datasets.

To motivate users to submit high quality data for mobile crowdsensing (MCS), some quality-aware incentive mechanisms have been proposed, which recruit and pay users strategically. However, in the existing mechanisms, the recruitment based only on tasks matching degree leads to the ineffective insistent data quality incentive. Meanwhile, the absence of the reasonable payment strategy cannot motivate users to submit high quality data in the current task. To address the above problems, we propose anOnlineall-phase quality-awareincentive mechanism (Oasis) to realize the quality incentive in both recruitment and payment phases. With the knapsack secretary, Oasis first devises a quality-aware pre-budgeting recruitment strategy, which decides whether the arriving user's long-term data quality and bid satisfy the recruited criterion. Then, in the payment phase, Oasis evaluates and updates the current and long-term data qualities of users. Based on the evaluation results, a two-level payment strategy is devised employing the Myerson theorem, where users submitting higher quality data can obtain more utilities under the budget constraint. Theoretical analysis proves that Oasis satisfies economic feasibility and constant competitiveness while achieving quality incentive in recruitment and payment phases. Extensive experiments using the real-world dataset demonstrate that the sensing result accuracy of Oasis increases 67% compared with the existing works.

Spatial range query enjoys widespread application scenarios due to the ever-growing geo-positioning technology in recent years. Huge amounts of encrypted geo-location data are being outsourced to cloud servers to alleviate local storage and computational overheads without leaking sensitive information. However, most existing Privacy-preserving Spatial Range Query (PSRQ) cannot achieve high efficiency while satisfying strong security over large-scale encrypted spatial data. To strike a best possible balance between security and efficiency, we propose a novel efficient Privacy-preserving Spatial Range Query (eP-SRQ) scheme in dual-cloud architecture over large-scale dataset. Specifically, we propose an efficient PSRQ scheme by designing a novel index structure based on Geohash algorithm, Circular Shift and Coalesce Zero-Sum Garbled Bloom Filter (CSC-ZGBF) and Symmetric Homomorphic Encryption (SHE), which makes the computational complexity of query process independent of dataset size. Formal security analysis proves that our scheme can achieve Indistinguishability against Chosen-Plaintext Attack (IND-CPA), and extensive experiments prove that our scheme is feasible in real-world applications.

Abstract Machine learning has become an integral part of modern intelligent systems in all aspects of life. Membership inference attacks (MIAs), as the significant model attacks, also jeopardize the privacy of the intelligent systems. Previous works on defending MIAs concentrate on the model output perturbation or tampering with the training process. However, data and model reuse are common in intelligent systems, which results in the lack of scalability of previous defending works. This paper proposes a new privacy‐preserving framework for images to transform source data into synthetic data to train models against MIAs. The synthetic data makes it easy to defend MIAs during data and model reuse to improve the scheme's scalability. The framework generates synthetic data satisfying differential privacy through the variational autoencoder model's information extraction and data generation capabilities to improve model accuracy. A noise addition mechanism with metric privacy for the latent code generated from source data is proposed, where noise is the product of Γ‐distribution and unit hyper‐sphere samples. Moreover, it is proved that the synthetic data also satisfies metric privacy. The experimental evaluations demonstrate that the framework reduces MIAs' attack accuracy to about 0.5 and maintains higher utility than DP‐SGD under the same setting.

In unmanned aerial vehicle (UAV) performing tasks, the UAV often faces electricity shortages. The traditional scheme to charge a UAV needs to return to the ground. Using the charging UAV (CUAV) can avoid the waste of electricity caused by the return. However, the existing works only consider a fixed charging location for electricity replenishment. Moreover, fewer works focus on the matching relationship between multi-CUAV and multi-UAV. It is challenging to complete the expected charging work due to the mismatch between the electricity demand and supply. To address this problem, we propose a two-stage electricity scheduling scheme. Specifically, in the charging location selection stage, we solve the Nash equilibrium (NE) of flight consumption between CUAVs and UAVs through the exact potential game, thereby determining the accessible charging position. Then, in the electricity transaction stage, we adopt the Stackelberg game model to determine the Stackelberg equilibrium (SE) between the acceptance rate of CUAVs and the rejection rate of UAVs, ensuring that both CUAVs and UAVs are satisfied with the unit electricity prices and electricity demands. Based on the above two game stages, we propose a supply and demand scheduling (SDS) algorithm to achieve dynamic scheduling between CUAVs and UAVs. Theoretical analysis indicates the exits of NE and SE. Furthermore, the extensive experiments show that our scheme has significant advantages over the baselines in charging cost, charging price, and flight consumption.

To solve the data island issue in the distributed Internet of Things (IoT) without privacy leakage, privacy-preserving federated learning (PPFL) has been extensively explored in both academic and industrial fields. However, existing PPFL solutions still suffer from a single point of failure and incur untrusted aggregation results caused by a malicious central server, and even cause a loss of model accuracy in an asynchronous setting. To solve these issues, we propose a privacy-preserving asynchronous federated learning scheme by using blockchain. Specifically, we use blockchain to address single points of failure and untrustworthy aggregation results, implement reliable model aggregation utilizing a practical byzantine fault-tolerant protocol in an asynchronous setting, and leverage differential privacy to improve system robustness. Formal security analysis and convergence analysis demonstrate that the proposed scheme is secure and robust, and extensive experiments demonstrate that our scheme can effectively ensure the accuracy of the system when compared with state-of-the-art schemes.

To achieve the search over encrypted data in cloud server, Searchable Encryption (SE) has attracted extensive attention from both academic and industrial fields. The existing Bloom filter-based SE schemes can achieve similarity search, but will generally incur high false positive rates, and even leak the privacy of values in Bloom filters (BF). To solve the above problems, we first propose a basicPrivacy-preservingBloom filter-basedKeywordSearch scheme using the Circular Shift and Coalesce-Bloom Filter (CSC-BF) and Symmetric-key Hidden Vector Encryption (SHVE) technology (namely PBKS), which can achieve effective search while protecting the values in BFs. Then, we design a new index structure T-CSCBF utilizing theTwin Bloom Filter (TBF) technology. Based on this, we propose an improved scheme PBKS+, which assigns a unique inclusion identifier to each position in each BF with privacy protection. Formal security analysis proves that our schemes are secure against Indistinguishability under Selective Chosen-Plaintext Attack (IND-SCPA), and extensive experiments using real-world datasets demonstrate that our schemes are feasible in practice.

The rapid growth of the blockchain size is a major bottleneck hindering its implementations in data-heavy applications. Current efforts improve the distributed storage ways and transactions' storage mechanisms of blockchain, however, the blockchain distribution and integrity are destroyed. Simplified Payment Verification (SPV) is closely related to blockchain storage, but the current solutions did not explore the privacy-preserving SPV. In this paper, we propose a new storage structure for blockchain transactions, called Coloring Index (CI), to reduce the blockchain's space occupation. Specifically, we devise an index building algorithm to simply calculate the indices of transactions for the sake of information concealing. By improving the Coloring Embedder for multi-sets query, we can store the indices into the Coloring Embedder to achieve the structured storage of transactions with small space occupation. Using CI, SPV query proceeds without revealing the user's address, thereby achieving secure data sharing in applications such as the intelligent vehicles' communications and distributed IoT. We prove CI's security against malicious full nodes when establishing possible connections between the address and the user. The experiments show that blockchain systems using our CI store one time more transactions than Merkle tree and half more than Bloom filter.

Searchable Symmetric Encryption (SSE), which enables users to search over encrypted data without decryption, has gained increasing attention from both academic and industrial fields. However, existing SSE schemes either have low search efficiency or cannot support multi-keyword search, dynamic updates, and result verification simultaneously. To solve these problems, we propose a Verifiable and Dynamic Multi-keyword Search (VDMS) scheme over encrypted data by using the bitmap and RSA accumulator, which provides multi-keyword search over encrypted data in an efficient, verifiable and updated way. The bitmap is used as a data structure to build the indexes, which improves the search efficiency and reduces the storage space of the indexes. The RSA accumulator and bitmap are combined to verify the correctness of results. Formal security analysis proves that our VDMS is adaptively secure against Chosen-Keyword Attacks (CKA), and empirical experiments using a real-world dataset demonstrate that our VDMS is efficient and feasible in practical applications.

Symmetric Searchable Encryption (SSE) schemes facilitate searching over encrypted data, and have been extensively explored to improve function, efficiency or security. There are, however, additional functions that we need to consider in a real-world setting. For example, forward and backward privacy are required to adequately secure newly added documents and deleted documents in Dynamic SSE (DSSE) schemes, and support boolean search (that allows users to search over encrypted data using basic boolean operations) to achieve improved efficiency and retrieval accuracy. Therefore, in this article we first construct the Verifiable Boolean Search over encrypted data (VBS), and then improve VBS to achieve Forward and Backward privacy (VBS-FB). Finally, we formally prove the security of our proposed schemes, and evaluate their performance using real-world datasets.

Getting access to labeled datasets in certain sensitive application domains can be challenging. Hence, one may resort to transfer learning to transfer knowledge learned from a source domain with sufficient labeled data to a target domain with limited labeled data. However, most existing transfer learning techniques only focus on one-way transfer which may not benefit the source domain. In addition, there is the risk of a malicious adversary corrupting a number of domains, which can consequently result in inaccurate prediction or privacy leakage. In this paper, we construct a secure andVerifiable collaborativeTransferLearning scheme, VerifyTL, to support two-way transfer learning over potentially untrusted datasets by improving knowledge transfer from a target domain to a source domain. Furthermore, we equip VerifyTL with a secure and verifiable transfer unit employing SPDZ computation to provide privacy guarantee and verification in the multi-domain setting. Thus, VerifyTL is secure against malicious adversary that can compromise up to$n-1$out of$n$data domains. We analyze the security of VerifyTL and evaluate its performance over four real-world datasets. Experimental results show that VerifyTL achieves significant performance gains over existing secure learning schemes.

Spatial keyword queries are attractive techniques that have been widely deployed in real-life applications in recent years, such as social networks and location-based services. However, existing solutions neither support dynamic update nor satisfy the privacy requirements in real applications. In this article, we investigate the problem of Dynamic Searchable Symmetric Encryption (DSSE) for spatial keyword queries. First, we formulate the definition of DSSE for spatial keyword queries (namely, DSSESKQ) and extend the DSSE leakage functions to capture the leakages in DSSESKQ. Then, we present a practical DSSESKQ construction based on geometric prefix encoding inverted-index and encrypted bitmap. Rigorous security analysis proves that our construction can achieve not only forward/backward privacy but content privacy as well, which can resist the most existing leakage-abuse attacks. Evaluation results using real-world datasets demonstrate the efficiency and feasibility of our construction. Comparative analysis reveals that our construction outperforms state-of-the-art schemes in terms of privacy and performance, e.g., our construction is 175x faster than existing schemes with only 51% server storage cost.

The surging interest in cryptocurrency has revitalized the research for digital signature schemes with strong security. In particular, signature schemes are investigated to resist the malleability attacks in cryptocurrency platforms. However, existing signature schemes only conquer partial malleability attacks due to various sources of attacks. Other solutions of new transaction realizations cannot simultaneously avoid the malleability attacks on both standard and contract transactions. Furthermore, the malleability attack becomes more stubborn in fast clearing applications. In this paper, we propose SigNT, an accessional signature scheme with unmalleable transaction implementations. The key of SigNT is an improved interactive signature scheme for securely instant confirmation of transactions. Unlike standard signatures, this signature is generated by the owner and block producers. Combining it with several other optimizations (i.e., hash execution of intermediate transactions and secret-based claiming conditions), SigNT achieves complete resistance against malleability attacks in both the standard and contract transactions. As an example, we show an implementation in Bitcoin with the “providing a deposit” protocol. The security analysis and comparative experiments demonstrate that SigNT has the best resistance against malleability attacks than previous malleability solutions. Besides, better performance is achieved than other schemes.

With the rapid development of Location-Based Services (LBS), a large number of LBS providers outsource spatial data to cloud servers to reduce their high computational and storage burdens, but meanwhile incur some security issues such as location privacy leakage. Thus, extensive privacy-preserving LBS schemes have been proposed. However, the existing solutions using Bloom filter do not take into account the redundant bits that do not map information in Bloom filter, resulting in high computational overheads, and reveal the inclusion relationship in Bloom filter. To solve these issues, we propose an efficient Privacy-preserving Spatial Range Query (PSRQ) scheme by skillfully combining Geohash algorithm with Circular Shift and Coalesce Bloom Filter (CSC-BF) framework and Symmetric-key Hidden Vector Encryption (SHVE), which not only greatly reduces the computational cost of generating token but also speeds up the query efficiency on large-scale datasets. In addition, we design a Confused Bloom Filter (CBF) to confuse the inclusion relationship by confusing the values of 0 and 1 in the Bloom filter. Base on this, we further propose a more secure and practical enhanced scheme PSRQ+by using CBF and Geohash algorithm, which can support more query ranges and achieve adaptive security. Finally, formal security analysis proves that our schemes are secure against Indistinguishability under Chosen-Plaintext Attacks (IND-CPA) and PSRQ+achieves adaptive IND-CPA, and extensive experimental tests demonstrate that our schemes using million-level dataset improve the query efficiency by 100x compared with previous state-of-the-art solutions.

Federated Learning (FL) has been widely used in various fields such as financial risk control, e-government and smart healthcare. To protect data privacy, many privacy-preserving FL approaches have been designed and implemented in various scenarios. However, existing works incur high communication burdens on clients, and affect the training model accuracy due to non-Independently and Identically Distributed (non-IID) data samples separately owned by clients. To solve these issues, in this paper we propose a secure Model-Contrastive Federated Learning with improved Compressive Sensing (MCFL-CS) scheme, motivated by contrastive learning. We combine model-contrastive loss and cross-entropy loss to design the local network architecture of our scheme, which can alleviate the impact of data heterogeneity on model accuracy. Then we utilize improved compressive sensing and local differential privacy to reduce communication costs and prevent clients’ privacy leakage. The formal security analysis shows that our scheme satisfies (ε,δ)-differential privacy. And extensive experiments using five benchmark datasets demonstrate that our scheme improves the model accuracy by 3.45% on average of all datasets under the non-IID setting and reduces the communication costs by more than 95%, when compared with FedAvg.

With the rapid development of Intelligent Transportation System (ITS), a large number of spatial data are generated in ITS. Although outsourcing spatial data to the cloud server can reduce the high local computation and storage overheads, it will also lead to security and privacy issues. Therefore, it is necessary to have a survey to specifically summarize these advanced privacy-preserving spatial data query schemes. However, the existing surveys considering both location information and keywords of spatial data only summarize the spatial keyword query scheme in plaintext environment, they do not consider the privacy of spatial data. Although there are some surveys on privacy-preserving spatial data query, they only focus on the location information of spatial data without considering descriptive keywords. Therefore, to understand the progress and research trends in the field, we give a comprehensive survey on secure spatial data query in ITS to summarize and analyze the most advanced solutions. Then, we make a comprehensive and detailed comparison of existing solutions in terms of query function, index structure, time complexity, security, etc. Finally, we show some open challenges and potential research directions for privacy-preserving spatial data query.

Federated learning is good for building better cooperative intelligent transportation system (C-ITS). Intellectual property protection in C-ITS brings many benefits to all vehicles. Although the protection of model intellectual property by watermark has received much research attention, the existing works only deploy watermark in centralized models. Due to the difference of watermark distribution among vehicles, the global model accuracy of watermark in federated learning is significantly reduced or the local watermark is invalid. To solve these problems, we propose a multi-party entangled watermark algorithm in federated learning. Specifically, in the local training, we propose a watermark enhancement algorithm, which solves the problem of local watermark failure. Then, in the global aggregation, we propose an entanglement aggregation algorithm, which solves the problem of a great loss of global model accuracy. We conduct extensive experiments on public datasets to show the superiority of our proposal. The results show that our scheme can obtain more than 16% and 31% advantages in model accuracy and watermark success rate, respectively, compared with existing watermark schemes in federated learning.

With increasingly popular GPS-equipped mobile devices (e.g., smartphones, tablets, laptops), massive spatio-textual data has been outsourced to cloud servers for storage and analysis such as spatial keyword search. However, existing privacy-preserving spatial keyword query schemes only support coarse-grained non-temporal access control in single-user sharing scenarios, which does not scale well in time-related scenes such as message valid period. To solve the above issues, we propose Privacy-preserving Boolean Range Query with Temporal access control in mobile computing (PBRQ-T). Specifically, we first achieve PBRQ with linear search complexity using the adapted Gray code, Bloom filter, and Katz-Sahai-Waters encryption. Then, we provide fine-grained and temporal access control in PBRQ based on the forward/backward derivation function and attribute-based encryption, where PBRQ is executed only when the spatio-textual data is accessible. Finally, an enhanced PBRQ-T (i.e., PBRQ-T+) with faster-than-linear search complexity is proposed by constructing a Quadtree index structure. Our formal security analysis shows that data privacy and index privacy can be guaranteed during the query process. Our extensive experiments using a real-world dataset demonstrate the efficiency and feasibility of our schemes.

To ensure the security of outsourced data without affecting data availability, one can use Symmetric Searchable Encryption (SSE) to achieve search over encrypted data. Considering that query users may search with misspelled words, the fuzzy search should be supported. However, conventional privacy-preserving fuzzy multi-keyword search schemes are incapable of achieving the result verification and adaptive security. To solve the above challenging issues, in this paper we propose a Verifiable Fuzzy multi-keyword Search scheme with Adaptive security (VFSA). VFSA first employs the locality sensitive hashing to hash the misspelled and correct keywords to the same positions, then designs a twin Bloom filter for each document to store and mask all keywords contained in the document, next constructs an index tree based on the graph-based keyword partition algorithm to achieve adaptive sublinear retrieval, finally combines the Merkle hash tree structure with the adapted multiset accumulator to check the correctness and completeness of search results. Our formal security analysis shows that VFSA is secure under the IND-CKA2 model and achieves query authentication. Our empirical experiments using the real-world dataset demonstrate the practicality of VFSA.

Location-Based Services (LBSs) are one of the most frequently used mobile applications in the modern society. Geo-Indistinguishability (Geo-Ind) is a promising privacy protection model for LBSs since it can provide formal security guarantees for location privacy. However, Geo-Ind undermines the statistical location distribution of users on the LBS server because of perturbed locations, thereby disabling the server to provide distribution-based services (e.g., traffic congestion maps). To overcome this issue, we give a privacy definition, called DistPreserv, to enable the LBS server to acquire valid location distributions while providing users with strict location protection. Then we propose a privacy-preserving LBS scheme to benefit both users and the server, in which a location perturbation mechanism is designed to achieve the given definition under the guide of the incentive compatibility, and a retrieval area determination method is presented to ensure query accuracy of users by using the dynamic programming on the two-dimensional map plane. Finally, we theoretically prove that the designed mechanism can achieve the definition of DistPreserv and the property of incentive compatibility. Experimental explorations using a real-world dataset indicate that our proposal prominently improves the availability of users’ location distributions by over 90%, while providing high precision and recall of queries.

With the increasing popularity of GPS-equipped mobile devices in cloud-assisted fog computing scenarios, massive spatio-textual data is generated and outsourced to cloud servers for storage and analysis. Existing privacy-preserving range query or ranked keyword search schemes does not support a unified index, and are just applicable for the symmetric environment where all users sharing the same secret key. To solve this issue, we propose aPrivacy-preservingRankedSpatial keywordQuery in mobile cloud-assistedFog computing (PRSQ-F). Specifically, we design a novel comparable product encoding strategy that combines both spatial and textual conditions tightly to retrieve the objects in query range and with the highest textual similarity. Then, we use a new conversion protocol and attribute-based encryption to support privacy-preserving retrieval and malicious user traceability in the asymmetric environment where different query users have different keys. Furthermore, we construct an R-tree-based index to achieve faster-than-linear retrieval. Our formal security analysis shows that data security can be guaranteed. Our empirical experiments using a real-world dataset demonstrate the efficiency and feasibility of PRSQ-F.

Symmetric Searchable Encryption (SSE), as an ideal primitive, can ensure data privacy while supporting retrieval over encrypted data. However, existing multi-user SSE schemes require the data owner to share the secret key with all query users or always be online to generate search tokens. While there are some solutions to this problem, they have at least one weakness, such as non-supporting conjunctive query, result decryption assistance of the data owner, and unauthorized access. To solve the above issues, we propose an O wner-free Di stributed S ymmetric searchable encryption supporting C onjunctive query (ODiSC). Specifically, we first evaluate the Learning-Parity-with-Noise weak Pseudorandom Function (LPN-wPRF) in dual-cloud architecture to generate search tokens with the data owner free from sharing key and being online. Then, we provide fine-grained conjunctive query in the distributed architecture using additive secret sharing and symmetric-key hidden vector encryption. Finally, formal security analysis and empirical performance evaluation demonstrate that ODiSC is adaptively simulation-secure and efficient.

With the rapid development of cloud computing, it is popular for data owners to outsource massive data to the cloud server for data sharing. To protect the privacy of sensitive data, many searchable encryption schemes are proposed. However, most of the existing studies focus on the single-owner model. In practice, users need to query data from distributed owners one by one, which inevitably brings great communication and computation overheads. Moreover, it lacks a secure scheme that realizes the access control requirements of individual owners. In this article, we propose AESM$^{2}$, a new attribute-based encrypted search with ownership enhancement scheme for multi-owner and multi-user distributed systems. Our design enables users to search data from authorized owners with only one trapdoor. Owners can enforce owner level permission on users and encrypt their data individually with fine-grained attribute level permission. For practical consideration, we further devise an efficient revocation method of the owner level permission for users, where ciphertexts do not need to be updated. We formally define and prove the security of our design. Moreover, we implement a system prototype and analyze the performance from theoretical and experimental aspects. The evaluation results demonstrate that our scheme is effective and efficient.

Proof of Stake powered blockchains account for general trends in existing consensus mechanisms. However, existing PoS protocols are vulnerable to the nothing at stake and long range attacks, which allow attackers to gain unfair shares based on costless simulations and malicious sale information. In a decentralized setting, these securities are limited as each node is unregulated. To address these problems, we introduce a proof-of-hardware-stake (PohS) consensus mechanism and a regulatory mechanism based on a consortium blockchain. Our approach is implemented in a sharding blockchain to scale the consensus. Since any node on the network cannot fake information, the blockchain trustless won not be decreased by the reliance on a consortium blockchain. Under the competing rule of PohS consensus mechanism, adversaries can issue the long range attack with at most$10^{-3}$probability. The regulatory mechanism implements the regulation of costless simulations at the nothing stake attack. We prove that our design is secure (e.g.,51% and selfish mining) against adversarial stakes less than 51% by adopting the square root of stakes for competition. Our proof also highlights the security of double-spending and long range attacks. Simulations are conducted to compare the efficiency of our approach with Ethereum and Ouroboros.

With the popularity of location based services, spatial keyword query has become an important application. In order to save the storage and computational costs, most data owners will outsource the data to the cloud server, but this will lead to two problems, such as privacy leakage and heavy network bandwidth burden. To solve above problems, we propose a Privacy-preserving top-k Spatial Keyword queries based on Fog computing, namly PSKF. To further improve the search efficiency, we use the IR-tree to build the index and store it in the cloud server. Each fog server also saves a different subtree of the IR-tree, so that we can decide which fog server to participate in the query by pruning. Formal security analysis shows that our proposed PSKF achieves Indistinguishability under Known-Plaintext Attacks (IND-KPA), and extensive experiments demonstrate that our proposed scheme is efficient and feasible in practical applications.

Searchable encryption(SE) allows users to efficiently retrieve data over encrypted cloud data, but most existing SE schemes only support exact keyword search, resulting in false results due to minor typos or format inconsistencies of queried keywords. The fuzzy keyword search can avoid this limitation, but still incurs low search accuracy and efficiency. Besides, most of fuzzy keyword search schemes do not consider malicious cloud servers which may execute a fraction of search operations or forge some results due to various interest incentives such as saving computation or storage resources. To solve these problems, we propose an efficient and Verifiable Ranked Fuzzy Multi-keyword Search scheme, called VRFMS. VRFMS uses locality-sensitive hashing and bloom filter to implement fuzzy keyword search, and employs Term Frequency-Inverse Document Frequency(TF-IDF) to sort the relevant results. Aiming to further improve the search accuracy, we design an improved bi-gram keyword transformation method. Furthermore, the homomorphic MAC technique and a random challenge technique are utilized to verify the correctness and completeness of returned results, respectively. Formal security analysis and empirical experiments demonstrate that VRFMS is secure and efficient in practical applications, respectively.

Member Inference Attack (MIA) is a key measure for evaluating privacy leakage in Machine Learning (ML) models, aiming to distinguish private members from non-members by training the attack model. In addition to the traditional MIA, the recently proposed Generative Adversarial Network (GAN)-based MIA can help the adversary know the distribution of the victim's private dataset, thereby significantly improving attack accuracy. For traditional attacks and this new type of attack, previous defense schemes cannot handle the trade-off between privacy and utility well. To this end, we propose a defense solution using multi-model ensemble framework. Specifically, we train multiple submodels to hide membership signals and resist MIA, achieving reduced privacy leakage while guaranteeing the effectiveness of the target model. Our security analysis shows that our scheme can provide privacy protection while preserving model utility. Experimental results on widely used datasets show that our scheme can effectively resist MIAs with negligible utility loss.

Ranked keyword search over encrypted data has been extensively studied in cloud computing as it enables data users to find the most relevant results quickly. However, existing ranked multi-keyword search solutions cannot achieve efficient ciphertext search and dynamic updates with forward security simultaneously. To solve the above problems, we first present a basic Machine Learning-based Ranked Keyword Search (ML-RKS) scheme in the static setting by using the k-means clustering algorithm and a balanced binary tree. ML-RKS reduces the search complexity without sacrificing the search accuracy, but is still vulnerable to forward security threats when applied in the dynamic setting. Then, we propose an Enhanced ML-RKS (called ML-RKS$^{+}$) scheme by introducing a permutation matrix. ML-RKS$^{+}$prevents cloud servers from making search queries over newly added files via previous tokens, thereby achieving forward security. The security analysis proves that our schemes protect the privacy of indexes, query tokens and keywords. Empirical experiments using the real-world dataset demonstrate that our schemes are efficient and feasible in practical applications.

Cloud storage provides Data Owners (DOs) with flexible data storage and management services, but simultaneously poses various security concerns, of which the integrity of outsourced data, as the most important security issue, determines the widespread use of cloud storage services. In this article, we present an$\mathbf {\overline{A}}$nonymous$\mathbf {\overline{A}}$uthorized$\mathbf {\overline{A}}$uditing scheme over$\mathbf {\overline{K}}$eyword-based$\mathbf {\overline{S}}$earchable$\mathbf {\overline{C}}$iphertexts (AAA-KSC) in cloud storage systems. In AAA-KSC, we devise an anonymous authorization mechanism, only the designated Third Party Auditor (TPA) could decrypt DO's identity and execute auditing tasks. In particular, by utilizing the verification metadata, without breaking the privacy of DO's concerned keyword, TPA could check the integrity of outsourced ciphertexts containing the specific keyword. More attractively, we design an identity-based homomorphic signature algorithm that makes use of the polynomial encapsulation mechanism to tackle with large scale data files, and thus AAA-KSC enables TPA to fulfil integrity verification with nearly constant computational costs, which are independent of the number of encrypted files that contain the same keyword. We provide the correctness, and prove that AAA-KSC is adequately secure. The performance evaluation experimentally demonstrates the high efficiency and feasibility of AAA-KSC in the real applications of cloud storage systems.

The encrypted image retrieval technique allows users to retrieve images in an encrypted manner without decrypting images. However, most of the existing schemes still are vulnerable to security threats and inefficiency, caused by malicious users and inefficient feature extraction methods, respectively. To this end, we propose a traceable encrypted image retrieval in the multi-user setting in this article, termed as MU-TEIR. First, MU-TEIR employs a convolutional neural network VGG16 to extract image feature vectors and calculate the mean and variance of the feature vectors to construct the index, then encrypts index with the distributed two trapdoors public-key cryptosystem. After that, MU-TEIR protects image content by encrypting each image pixel with a standard stream cipher. Furthermore, MU-TEIR utilizes a watermark-based mechanism to prevent malicious query users from maliciously distributing images. Detailed security analysis shows that MU-TEIR protects the outsourced images and indexes security as well as query privacy, and can track malicious users. Experimental results verify effectiveness of MU-TEIR.

Federated learning (FL) was once considered secure for keeping clients’ raw data locally without relaying on a central server. However, the transmitted model weights or gradients still reveal private information, which can be exploited to launch various inference attacks. Moreover, FL based on deep neural networks is prone to the curse of dimensionality. In this paper, we propose a compressed and privacy-preserving FL scheme in DNN architecture by using Compressive sensing and Adaptive local differential privacy (called as CAFL). Specifically, we first compress the local models by using Compressive Sensing (CS), then adaptively perturb the remaining weights according to their different centers of variation ranges in different layers and their own offsets from corresponding range centers by using Local Differential Privacy (LDP), finally reconstruct the global model almost perfectly by using the reconstruction algorithm of CS. Formal security analysis shows that our scheme achieves ϵ-LDP security and introduces zero bias to estimating average weights. Extensive experiments using MINIST and Fashion-MINIST datasets demonstrate that our scheme with minimum compression ratio 0.05 can reduce the number of parameters by 95%, and with a lower privacy budget ϵ = 1 can improve the accuracy by 80% on MINIST and 12.7% on Fashion-MINIST compared with state-of-the-art schemes.

With the rapid development of geographic location technology and the explosive growth of data, a large amount of spatio-textual data is outsourced to the cloud server to reduce the local high storage and computing burdens, but at the same time causes security issues such as data privacy leakage. Thus, extensive privacy-preserving spatial keyword query schemes have been proposed. Most of the existing schemes use Asymmetric Scalar-Product-Preserving Encryption (ASPE) for encryption, but ASPE has proven to be insecure. And the existing spatial range query schemes require users to provide more information about the query range and generate a large amount of ciphertext, which causes high storage and computational burdens. To solve these issues, in this paper we introduce some random numbers and a random permutation to enhance the security of ASPE scheme, and then propose a novel privacy-preserving Spatial Keyword Query (SKQ) scheme based on the enhanced ASPE and Geohash algorithm. In addition, we design a more Lightweight Spatial Keyword Query (LSKQ) scheme by using a unified index for spatial range and multiple keywords, which not only greatly decreases SKQ’s storage and computational costs but also requires users to provide little information about query region. Finally, formal security analysis proves that our schemes have Indistinguishability under Chosen Plaintext Attack (IND-CPA), and extensive experiments demonstrate that our enhanced scheme is efficient and practical.

Key-value store is adopted by many applications due to its high performance in processing big data workloads. Recent research on secure cloud storage has shown that even if the data is encrypted, attackers can learn the sensitive information of data by launching access pattern attacks such as frequency analysis. For this issue, some schemes have been proposed to protect encrypted key-value stores against access pattern attacks. However, existing solutions protect access pattern information at the cost of large storage and bandwidth overhead, which is unacceptable for large-scale key-value stores. In this paper, we devise a K-indistinguishable frequency smoothing scheme for encrypted key-value stores, which can resist access pattern attacks launched by passive persistent adversaries with minimal storage and bandwidth overhead. Then, we propose a dynamic K-indistinguishable frequency smoothing scheme. It can efficiently adapt to the changes in access distribution while ensuring the K-indistinguishable security level and bandwidth efficiency. Finally, we formally analyze the security of our design. Extensive experiments demonstrate that our design achieves high throughput while minimizing storage and bandwidth overhead.

Ciphertext retrieval under heterogeneous data sets is a critical concern in the cloud-assisted Internet of Things (IoT). Previous ciphertext retrieval schemes eliminate computation burden and security concerns to some extent, but cannot support the encrypted heterogeneous data sets. To solve this problem, in this article we propose a transparent ciphertext retrieval system regardless of programming language, accessible platforms, and heterogeneity of data sets. The retrieval system is a novel combination of the proposed access and integration middleware with a current encrypted cloud database system. The middleware enables cross-language and cross-platform queries over various database systems, besides, it schedules the designed data integration method in heterogeneous databases to realize cross-database queries over the encrypted cloud data sets. The proposed ciphertext retrieval system achieves efficient data sharing among different IoT applications with various database systems. We prove that the system guarantees data security by avoiding malicious hackers. Extensive experiments show that our design is efficient and feasible in practice.

Controller area network (CAN) bus anomaly detection on the Internet of Vehicles (IoV) is a topic of ongoing interest and increasing importance, particularly as IoV becomes commonplace. However, existing abnormal detection schemes are not ideal due to the lack of abnormal data in IoV and the difficulty of parsing the diverse message rules in existing vehicles. While single-classification of the support vector domain description (SVDD) algorithm can detect abnormalities only with normal message information, the approach has a high rate of false negatives when deployed directly in the in-vehicle network environment. In addition, the real-time data generation (e.g., IoV messages) compounds the challenge of designing effective detection approaches. Therefore, this article proposes a mechanism for car networking message classification for a broad range of data, and establishes a weak model for many simple redundant data in the vehicle Intranet. The detection can reduce the time and computation costs of detection while ensuring accuracy. Then, this article proposes two improved SVDD schemes: 1) M-SVDD scheme, which adds the Markov chain to detect time-related messages and 2) G-SVDD scheme, which maps the kernel function to the Gaussian kernel function to reduce the redundant area of the model and the false-negative rate. The experimental results show that our proposed schemes have higher accuracy, recall rate, and fewer computation overhead.

Internet of Things (IoT) devices and systems are becoming increasingly commonplace, and as such systems scale up, so do the computational and storage requirements. Hence, one recent trend is to outsource data from IoT devices to remote systems. To facilitate both ciphertext retrieval and data confidentiality in the outsourced data, a number of searchable encryption (SE) approaches have been proposed in the literature. However, due to limited keyword space, a number of SE schemes are vulnerable to keyword guessing attacks (KGAs). In addition, existing SE approaches generally do not consider the hierarchical structure in which users at different levels require varying access privileges. Furthermore, existing SE schemes seldom provide time-controlled access control. Therefore, in this article, we propose a time-controlled hierarchical multikeyword search by using a double-server architecture to mitigate KGA. In our approach, we also build a public key tree to support different access permissions for hierarchical users. Formal security analysis shows that our scheme is secure, and extensive experiments demonstrate that our scheme is practical.

Encrypted image retrieval is a promising technique for achieving data confidentiality and searchability the in cloud-assisted Internet of Things (IoT) environment. However, most of the existing top-$k$ranked image retrieval solutions have low retrieval efficiency and may leak the values and orders of similarity scores to the cloud server. Hence, if a malicious server learns user background information through some improper means, then the malicious server can potentially infer user preferences and guess the most similar image content according to similarity scores. To solve the above challenges, we propose a privacy-preserving threshold-based image retrieval scheme using the convolutional neural network (CNN) model and a secure$k$-nearest neighbor (kNN) algorithm, which improves the retrieval efficiency and prevents the cloud server from learning the values and orders of similarity scores. Formal security analysis shows that our proposed scheme can resist both ciphertext-only attack (COA) and chosen-plaintext attack (CPA), and extensive experiments demonstrate that our proposed scheme is efficient and feasible for real-world data sets.

Cloud-assisted Industrial Internet of Things (IIoT) systems are increasingly deployed in various applications such as location-based services. Outsourcing data to cloud servers can help minimize local data storage and computation overheads, but it may introduce security and privacy concerns. Therefore, privacy-preserving spatial keyword search has been extensively explored in the literature. However, existing solutions still reveal the order of the spatio-textual similarity values between the query point and all data objects, and do not support searching for arbitrary geometric regions. To solve these issues, in this article we propose a privacy-preserving threshold spatial keyword search (TSKS) scheme. Specifically, we use the polynomial fitting technology, vector space model, and randomizable matrix multiplication technology to allow the cloud server to find relevant objects that are within some arbitrary geometric range and contain all query keywords. Finally, formal security analysis proves that our scheme can protect the privacy of data sets and queries, and extensive experiments demonstrate that our scheme is efficient and practical.

The increasing awareness in privacy has partly contributed to the renewed interest in privacy-preserving encrypted image retrieval, and designing for outsourced images stored on cloud servers, etc. However, there are some limitations in these existing schemes such as low retrieval accuracy, low retrieval efficiency, and less efficient result verification in the dynamic setting. Therefore, in this paper we present a novel Dynamic Verifiable Retrieval over Encrypted Images (DVREI) scheme. First, a pre-trained Convolutional Neural Network (CNN) model is utilized to extract image features to improve retrieval accuracy. Then, an encrypted index based on the K-means clustering algorithm is designed to improve retrieval efficiency. Finally, a dynamic verification tree based on the chameleon hash is used to verify the correctness of the retrieval results and support dynamic updates. We theoretically and experimentally evaluate the security and performance of DVREI to demonstrate its practicability.

With the emergence of intelligent terminals, the Content-Based Image Retrieval (CBIR) technique has attracted much attention from many areas (i.e., cloud computing, social networking services, etc.). Although existing privacy-preserving CBIR schemes can guarantee image privacy while supporting image retrieval, these schemes still have inherent defects (i.e., low search accuracy, low search efficiency, key leakage, etc.). To address these challenging issues, in this article we provide a similarity Search for Encrypted Images in secure cloud computing (called SEI). First, the feature descriptors extracted by the Convolutional Neural Network (CNN) model are used to improve search accuracy. Next, an encrypted hierarchical index tree by using$K$-means clustering based on Affinity Propagation (AP) clustering is devised, which can improve search efficiency. Then, a limited key-leakage k-Nearest Neighbor (kNN) algorithm is proposed to protect key from being completely leaked to untrusted image users. Finally, SEI is extended to further prevent image users’ search information from being exposed to the cloud server. Our formal security analysis proves that SEI can protect image privacy as well as key privacy. Our empirical experiments using a real-world dataset illustrate the higher search accuracy and efficiency of SEI.

With the advent of cloud computing, explosively increasing images are gradually outsourced to the cloud server for costs saving and feasibility. For security and privacy concerns, images (e.g., medical diagnosis, personal photos) should be encrypted before being outsourced. However, traditional encrypted image retrieval techniques still suffer from costly access control and low search accuracy. To solve these challenging issues, in this article, we first propose a Controllable encrypted cloud image Search scheme in Multi-user settings (namely CSM) by using the polynomial-based access strategy and proxy re-encryption technique. CSM achieves efficient access control and avoids heavy communication overhead caused by key transmission. Then, we improve the basic CSM to achieve malicious search user Tracing (namely TCSM) by utilizing the watermark technique, which can further prevent search users from illegally redistributing retrieved images to unauthorized search users. Our formal security analysis proves that our CSM (or TCSM) can guarantee the privacy of images, indexes, and search queries. Our empirical experiments using real-world datasets demonstrate the efficiency and high accuracy of our CSM (or TCSM) in practice.

With the explosive growth of data volume in the cloud computing environment, data owners are increasingly inclined to store their data on the cloud. Although data outsourcing reduces computation and storage costs for them, it inevitably brings new security and privacy concerns, as the data owners lose direct control of sensitive data. Meanwhile, most of the existing ranked keyword search schemes mainly focus on enriching search efficiency or functionality, but lack of providing efficient access control and formal security analysis simultaneously. To address these limitations, in this article we propose an efficient and privacy-preservingMulti-keywordRankedSearch scheme withFine-grained access control (MRSF). MRSF can realize highly accurate ciphertext retrieval by combining coordinate matching with Term Frequency-Inverse Document Frequency (TF-IDF) and improving the secure$k$NN method. Besides, it can effectively refine users’ search privileges by utilizing the polynomial-based access strategy. Formal security analysis shows that MRSF is secure in terms of confidentiality of outsourced data and the privacy of index and tokens. Extensive experiments further show that, compared with existing schemes, MRSF achieves higher search accuracy and more functionalities efficiently.

With cloud services, data users can retrieve encrypted data while preserving data confidentiality. However, this new paradigm suffers from many security concerns. A major concern is how to avoid insider Keyword-Guessing Attacks (KGA), which implies that the internal attackers can guess the candidate keywords successfully in an off-line manner. To address this issue, recently, two verifiable searchable encryption schemes (published in IEEE Transactions on Cloud Computing, doi: 10.1109/TCC.2020.2989296) in cloud storage were proposed which enjoys many desirable features. In this letter, we demonstrate that the schemes are insecure against insider keyword-guessing attack. Specifically, we show that the adversary can derive the keywords in an off-line manner.

Searchable Encryption (SE) is a popular cryptographic primitive for building ciphertexts retrieval systems with far-reaching applications. However, existing SE schemes generally do not support threshold access control (i.e., data users must collaboratively issue search and decryption operations over encrypted cloud data) in a group-oriented cloud data sharing setting, which is increasingly receiving much attention in the research community. Thus, in this article, we first propose a Threshold Multi-keyword Search (TMS) scheme for cloud-based group data sharing (referred to as basic TMS scheme) by utilizing Shamir’s secret sharing technique, to achieve threshold multi-keyword search, threshold decryption, and short record ciphertext size. Then, we extend this basic TMS to realize threshold result verification and threshold traceability (referred to as enhanced TMS). Furthermore, the enhanced TMS is extended to support public result verification and dynamic operations with the public verifier and improved hash tables, respectively. Our formal security analysis proves that both basic TMS and enhanced TMS are semi-adaptively secure and can resist Chosen-Keyword Attack (CKA). Our theoretical evaluation and empirical experiments demonstrate the potential utility of both schemes.

Searchable encryption (SE) allows cloud tenants to retrieve encrypted data while preserving data confidentiality securely. Many SE solutions have been designed to improve efficiency and security, but most of them are still susceptible to insider Keyword-Guessing Attacks (KGA), which implies that the internal attackers can guess the candidate keywords successfully in an off-line manner. Also in existing SE solutions, a semi-honest-but-curious cloud server may deliver incorrect search results by performing only a fraction of retrieval operations honestly (e.g., to save storage space). To address these two challenging issues, we first construct the basic Verifiable SE Framework (VSEF), which can withstand the inside KGA and achieve verifiable searchability. Based on the basic VSEF, we then present the enhanced VSEF to support multi-keyword search, multi-key encryption and dynamic updates (e.g., data modification, data insertion, and data deletion) at the same time, which highlights the importance of practicability and scalability of SE in real-world application scenarios. We conduct extensive experiments using the Enron email dataset to demonstrate that the enhanced VSEF achieves high efficiency while resisting to the inside KGA and supporting the verifiability of search results.

Cloud-assisted Internet of Things (IoT) is increasingly prevalent used in various fields, such as the healthcare system. While in such a scenario, sensitive data (e.g., personal electronic medical records) can be easily revealed, which incurs potential security challenges. Thus, Symmetric Searchable Encryption (SSE) has been extensively studied due to its capability of supporting efficient search on encrypted data. However, most SSE schemes require the data owner to share the complete key with query users and take malicious cloud servers out of consideration. Seeking to address these limitations, in this article we propose a Verifiable Privacy-preserving data Search scheme with Limited key-disclosure (VPSL) for cloud-assisted Internet of Things. VPSL first designs a trapdoor generation protocol for obtaining a trapdoor with disclosing limited key information and without revealing plaintext query points to others. Then, VPSL provides an efficient result verification and search processing by employing the Merkle hash tree structure and k-means clustering technique, respectively. VPSL is secure against the level-2 attack. Finally, an enhanced VPSL (called VPSL+) resisting the level-3 attack is constructed by introducing the random splitting technique. Empirical experiments demonstrate the accuracy and efficiency of VPSL or VPSL+ using real-world datasets.

Blockchain allows for secure management of a shared ledger by agreement protocols, where transactions are validated over network without central authorities. Although the agreement protocol has been thoroughly conducted of propagation and consensus researches, mobility of nodes in unstructured overlay networks has not received much attention. Besides, current dynamic propagation schemes waste travel hops and are low of delivery ratio. In this article, we propose a social characteristic-based propagation-efficient protocol NefSBFT to agree on system state plus consensus mechanisms in public blockchains. We devise a propagation technique (travel hops of at least$\frac{1}{3}$savings, delivery ratio above 0.93, etc.) for message multicasting when exploiting real nodes’ social characteristics of intermittent connectivity and frequent partitions. This propagation technique is executed in the improved FastBFT to achieve transaction ordering and block verification, thus, no controllable mobility is required during the whole system’s execution. NefSBFT achieves fast propagation, small message complexity and few resource consumption of travel hops and running nodes for complete protocol execution. We analyze NefSBFT’s security against DDOS attack of non-primary failure. The experiments show the performance tradeoff under different parameters, compare the propagation efficiency with Erlay and Flooding, and clarify NefSBFT’s impact on the whole system performance through comparison.

Privacy-Preserving Federated Learning (PPFL) is an emerging secure distributed learning paradigm that aggregates user-trained local gradients into a federated model through a cryptographic protocol. Unfortunately, PPFL is vulnerable to model poisoning attacks launched by a Byzantine adversary, who crafts malicious local gradients to harm the accuracy of the federated model. To resist model poisoning attacks, existing defense strategies focus on identifying suspicious local gradients over plaintexts. However, the Byzantine adversary submits encrypted poisonous gradients to circumvent existing defense strategies in PPFL, resulting in encrypted model poisoning. To address the issue, in this paper we design a privacy-preserving defense strategy using two-trapdoor homomorphic encryption (referred to as ShieldFL), which can resist encrypted model poisoning without compromising privacy in PPFL. Specially, we first present the secure cosine similarity method aiming to measure the distance between two encrypted gradients. Then, we propose the Byzantine-tolerance aggregation using cosine similarity, which can achieve robustness for both Independently Identically Distribution (IID) and non-IID data. Extensive evaluations on three benchmark datasets (i.e.,MNIST, KDDCup99, and Amazon) show that ShieldFL outperforms existing defense strategies. Especially, ShieldFL can achieve 30%-80% accuracy improvement to defend two state-of-the-art model poisoning attacks in both non-IID and IID settings.

Federated learning enables clients to train a machine learning model jointly without sharing their local data. However, due to the centrality of federated learning framework and the untrustworthiness of clients, traditional federated learning solutions are vulnerable to poisoning attacks from malicious clients and servers. In this paper, we aim to mitigate the impact of the central server and malicious clients by designing a Privacy-preserving Byzantine-robust Federated Learning (PBFL) scheme based on blockchain. Specifically, we use cosine similarity to judge the malicious gradients uploaded by malicious clients. Then, we adopt fully homomorphic encryption to provide secure aggregation. Finally, we use blockchain system to facilitate transparent processes and implementation of regulations. Our formal analysis proves that our scheme achieves convergence and provides privacy protection. Our extensive experiments on different datasets demonstrate that our scheme is robust and efficient. Even if the root dataset is small, our scheme can achieve the same efficiency as FedSGD.

Acquiring the spatial distribution of users in mobile crowdsensing (MCS) brings many benefits to users (e.g.,avoiding crowded areas during the COVID-19 pandemic). Although the leakage of users’ location privacy has received a lot of research attention, existing works still ignore the rationality of users, resulting that users may not obtain satisfactory spatial distribution even if they provide true location information. To solve the problem, we employ game theory with incomplete information to model the interactions among users and seek an equilibrium state through learning approaches of the game. Specifically, we first model the service as a game in the satisfaction form and define the equilibrium for this service. Then, we design aLEFSalgorithm for the privacy strategy learning of users when their satisfaction expectations are fixed, and further designLSREthat allows users to have dynamic satisfaction expectations. We theoretically analyze the convergence conditions and characteristics of the proposed algorithms, along with the privacy protection level obtained by our solution. We conduct extensive experiments to show the superiority and various performances of our proposal, which illustrates that our proposal can get more than 85% advantage in terms of the sensing distribution availability compared to the traditional spatial cloaking based solutions.

As a novel network infrastructure that realizes the interconnection of humans, machines, and things, 5G enables a large number of devices with communication and sensing capabilities to securely, quickly, and reliably connect to the Internet and facilitates Industrial Internet of Things (IIoT) applications such as smart cities, smart homes, and smart grids. The 5G technique has potential applications in various military and civilian settings such as radar anomaly detection, unmanned aerial vehicles (UAV) data storage sharing [A1], [A2], and traffic data analysis.

With the large-scaled data generated from various interconnected machines and networks, Industrial Internet of Things (IIoT) provides unprecedented opportunities for facilitating data mining for industrial applications. The current IIoT architecture tends to adopt cloud computing for further timely mining IIoT data, however, the openness of security-critical IIoT becomes challenging in terms of unbearable privacy issues. Most existing privacy-preserving data mining (PPDM) techniques are designed to resist honest-but-curious adversaries (i.e., cloud servers and data users). Due to the complexity and openness in IIoT, PPDM is significantly difficult with the presence of malicious adversaries in IIoT who may incur incorrect learned models and inference results. To solve the aforementioned issues, we propose a framework to extend existing PPDM to guard linear regression against malicious behaviors (hereafter referred to as GuardLR). To prevent dishonest computations of cloud servers and inconsistent inputs of data users, we first design a privacy-preserving verifiable learning scheme for linear regression, which guarantees the correctness of learning. In this article, to avoid malicious clouds from returning incorrect inference results, we design a privacy-preserving prediction scheme with lightweight verification. Our formal security analysis shows that GuardLR achieves privacy, completeness, and soundness. Empirical experiments using real-world datasets also demonstrate that GuardLR has high computational efficiency and accuracy.

The development of Industrial Internet of Things (IIoT) provides massive abundant data resources for trading and mining. However, the existing data trading schemes achieve data usage control at the cost of high latency, thereby resulting in poor service quality as the values of IIoT data degrade over time. This article proposes a monitor-based usage control model to enforce data usage policies on the user side, which eliminates frequent interactions between owners and users. Based on that, a data trading scheme with efficient usage control for IIoT (called DTSI) is devised, which utilizes blockchain smart contract and software guard extensions (SGX) to enable owners to fully control users’ identities and operations at minimal overhead. Security analysis shows that DTSI effectively prevents data abuse and ensures the fair exchange of data. Meanwhile, extensive experiments are conducted on the DTSI prototype comparing with the state-of-the-art schemes with real-world IIoT datasets, which demonstrates the efficiency of DTSI.

Distributed fog computing has received wide attention recently. It enables distributed computing and data management on the network nodes within the close vicinity of IoT devices. An important service of fog-cloud based systems is data deduplication. With the increasing concern of privacy, some privacy-preserving data deduplication schemes have been proposed. However, they cannot support lossless deduplication of encrypted similar data in the fog-cloud network. Meanwhile, no existing design can protect message equality information while resisting brute-force and frequency analysis attacks. In this paper, we propose a privacy-preserving and compression-based data deduplication system under the fog-cloud network, which supports lossless deduplication of similar data in the encrypted domain. Specifically, we first use the generalized deduplication technique and cryptographic primitives to implement secure deduplication over similar data. Then, we devise a two-level deduplication protocol that can perform secure and efficient deduplication at distributed fog nodes and the cloud. The proposed system can not only resist brute-force and frequency analysis attacks but also ensure that only the data operator can capture the message equality information. We formally analyze the security of our design. Performance evaluations demonstrate that our proposed design is efficient in computing, storage, and communication.

Crowdsourcing has attracted widespread attention in recent years and developed into various applications. An indispensable service of crowdsourcing systems is task recommendation, which means tasks should be accurately recommended to the workers with aligned interests. However, existing systems rely on their separate servers to conduct recommendation services, resulting in computing resources locked inside each isolated system. Moreover, due to the wide attacking surfaces of traditional centralized servers setting, existing systems are subject to single points of failure or malicious data breaches. Therefore, failure to address these inherent limitations properly will hinder the wide adoption of crowdsourcing. In this article, we propose and implement FedCrowd, the first federated and privacy-preserving crowdsourcing platform by using blockchain technology. Our main idea is to employ the smart contract as a trusted platform for systems to release encrypted tasks, and carefully craft matching protocols to enable efficient task recommendations in the ciphertext domain. Our task-matching protocols are highly customized for the decentralized settings, where users can securely perform keyword and range-based queries over federated task indexes without sharing secret keys. We formally analyze the security strengths and complete the prototype implementation on Ethereum. Experiment results demonstrate the feasibility and usability of the FedCrowd platform.

Ranked keyword search has gained Ranked keyword search has gained traction due to its attractive properties such as flexibility and accessibility. However, most existing ranked keyword search schemes ignore the semantic associations between the documents and queries. To solve this challenging issue in cloud-assisted edge computing, we first design theSemantic-awareRankedMulti-keywordSearch (SRMS) scheme by adopting the Latent Dirichlet Allocation (LDA) topic model and the Chinese Remainder Theorem (CRT)-based secret sharing mechanism. Considering that the cloud server may be malicious, we implement a basic verification mechanism in SRMS to verify the correctness and completeness of search results and extend this verification mechanism in cloud-assisted edge computing scenarios. Formal security analysis proves that SRMS and extended result verification mechanisms are secure in both the known ciphertext model and the known background model. Extensive experiments using the real-world dataset demonstrate that SRMS is efficient and practical.

Federated learning has become prevalent in medical diagnosis due to its effectiveness in training a federated model among multiple health institutions (i.e., Data Islands (DIs)). However, increasingly massive DI-level poisoning attacks have shed light on a vulnerability in federated learning, which inject poisoned data into certain DIs to corrupt the availability of the federated model. Previous works on federated learning have been inadequate in ensuring the privacy of DIs and the availability of the final federated model. In this article, we design a secure federated learning mechanism with multiple keys to prevent DI-level poisoning attacks for medical diagnosis, calledSFAP. Concretely,SFAPprovides privacy-preserving random forest-based federated learning by using the multi-key secure computation, which guarantees the confidentiality of DI-related information. Meanwhile, a secure defense strategy over encrypted locally-submitted models is proposed to resist DI-level poisoning attacks. Finally, our formal security analysis and empirical tests on a public cloud platform demonstrate the security and efficiency ofSFAPas well as its capability of resisting DI-level poisoning attacks.

With the development of machine learning, it is popular that mobile users can submit individual symptoms at any time anywhere for medical diagnosis. Edge computing is frequently adopted to reduce transmission latency for real-time diagnosis service. However, the data-driven machine learning, which requires to build a diagnosis model over vast amounts of medical data, inevitably leaks the privacy of medical data. It is necessary to provide privacy preservation. To solve above challenging issues, in this article, we design a lightweight privacy-preserving medical diagnosis mechanism on edge, called LPME. Our LPME redesigns the extreme gradient boosting (XGBoost) model based on the edge-cloud model, which adopts encrypted model parameters instead of local data to remove amounts of ciphertext computation to plaintext computation, thus realizing lightweight privacy preservation on resource-limited edge. In addition, LPME provides secure diagnosis on edge with privacy preservation for private and timely diagnosis. Our security analysis and experimental evaluation indicates the security, effectiveness, and efficiency of LPME.

To ensure the security of images outsourced to the malicious cloud without affecting searchability on such outsourced (typically encrypted) images, one could use privacy-preserving Content-Based Image Retrieval (CBIR) primitive. However, conventional privacy-preserving CBIR schemes based on Searchable Symmetric Encryption (SSE) are not capable of supporting efficient fine-grained access control and result verification simultaneously. Therefore, in this article, we propose aVerifiableFine-grained encryptedImageRetrieval scheme in theMulti-owner multi-user settings (VFIRM). VFIRM first utilizes a novel polynomial-based access strategy to provide efficient fine-grained access control. Then, it employs the dual secure$k$-nearest neighbor technique to distribute distinct keys to different data owners and data users, and finally implements an adapted homomorphic MAC technique to check the correctness of search results. Our formal security analysis shows that VFIRM is non-adaptive semantic secure if the client's search key is generated randomly and keeps in secret. Our empirical experiments using two real-world datasets (i.e., Caltech101 and Corel5k) demonstrate the practicality of VFIRM.

With the development of Mobile Healthcare Monitoring Network (MHMN), patients’ data collected by body sensors not only allows patients to monitor their health or make online pre-diagnosis but also enables clinicians to make proper decisions by utilizing data mining technique. However, sensitive data privacy is still a major concern. In this article, we propose practical techniques for searching and making online pre-diagnosis over encrypted data. First, we propose a new Diverse Keyword Searchable Encryption (DKSE) scheme which supports multi-dimension digital vectors range query and textual multi-keyword ranked search to gain a broad range of applications in practice. In addition, a framework called PRIDO based on the DKSE is designed to protect patients’ personal data in data mining and online pre-diagnosis. According to the PRIDO framework, we achieve privacy-preserving naïve Bayesian and decision tree classifiers and discuss its potential applications in actual deployments. Security analysis proves that patients’ data privacy can be well protected without loss of data confidentiality, and performance evaluation demonstrates the efficiency and accuracy in the diverse keyword search, data mining, and disease pre-diagnosis, respectively.

Nowadays, many data owners choose to outsource their data to public cloud servers while allowing authorized users to retrieve them. To protect data confidentiality from an untrusted cloud, many studies on searchable encryption (SE) are proposed for privacy-preserving search over encrypted data. However, most of the existing SE schemes only focus on the single-owner model. Users need to search one-by-one among data owners to retrieve relevant results even if data are from the same cloud server, which inevitably incurs unnecessary bandwidth and computation cost to users. Thus, how to enable efficient authorized search over multi-owner datasets remains to be fully explored. In this paper, we propose a new privacy-preserving search scheme for the multi-owner and multi-user model. Our proposed scheme has two main advantages: 1) We achieve an attribute-based keyword search for multi-owner model, where users can only search datasets from specific authorized owners. 2) Each data owner can enforce its own fine-grained access policy for users while an authorized user only needs to generate one trapdoor (i.e., encrypted search keyword) to search over multi-owner encrypted data. Through rigorous security analysis and performance evaluation, we demonstrate that our scheme is secure and feasible.

In the era of machine learning, mobile users are able to submit their symptoms to doctors at any time, anywhere for personal diagnosis. It is prevalent to exploit edge computing for real-time diagnosis services in order to reduce transmission latency. Although data-driven machine learning is powerful, it inevitably compromises privacy by relying on vast amounts of medical data to build a diagnostic model. Therefore, it is necessary to protect data privacy without accessing local data. However, the blossom has also been accompanied by various problems, i.e., the limitation of training data, vulnerabilities, and privacy concern. As a solution to these above challenges, in this paper, we design a lightweight privacy-preserving medical diagnosis mechanism on edge. Our method redesigns the extreme gradient boosting (XGBoost) model based on the edge-cloud model, which adopts encrypted model parameters instead of local data to reduce amounts of ciphertext computation to plaintext computation, thus realizing lightweight privacy preservation on resource-limited edges. Additionally, the proposed scheme is able to provide a secure diagnosis on edge while maintaining privacy to ensure an accurate and timely diagnosis. The proposed system with secure computation could securely construct the XGBoost model with lightweight overhead, and efficiently provide a medical diagnosis without privacy leakage. Our security analysis and experimental evaluation indicate the security, effectiveness, and efficiency of the proposed system.

With the development of the Mobile Healthcare Monitoring Network (MHMN), patients’ data collected by body sensors not only allows patients to monitor their health or make online pre-diagnosis but also enables clinicians to make proper decisions by utilizing data mining techniques. In MHMN, patients’ personal data are collected by sensors per second and uploaded to the cloud server as multi-dimension vectors, cloud server stores the personal data as well as sends monitoring information to the hospital when the real-time data is abnormal. Hospital users (i.e., doctors, etc.) may query some samples which contain certain textual keywords or digital keywords in certain ranges for disease diagnosis or medical research. For example, a certain hospital user may query all samples with textual keywords ‘cancer; diabetes’ and digital vectors ‘age’ ∈ [30,50], ‘blood sugar’ ∈ [4,8], ‘heart rhythm’ ∈ [70,80]. Besides, the potential value of massive medical data has attracted considerable interests recently, for example, valuable results in diagnosis model can be yield with large-scale aggregation analysis of personal medical data. The cloud server can build a diagnosis model using data mining technology over massive data, so that hospital users or pre-diagnosis users upload medical data (i.e., age, blood pressure, blood sugar, etc.) to the cloud for diagnosis.

As a potential application field of the sixth-generation (6G) communication technology and a promising part of massive Internet of Things (IoT), vehicular networks have attracted considerable attention from both academia and industry in recent years, where the cooperative safety applications are a significant branch. It is widely acknowledged that 6G is able to provide high-throughput and low-latency wireless communication capability for vehicular networks, support massive interconnectivity in vehicular networks with diverse service requirements, and significantly improve the performance of vehicular networks. Both trust management and privacy preservation play significant roles in vehicular networks, and there exists a tradeoff between them. For providing a satisfactory solution to balance the trust management and privacy preservation in vehicular networks, we put forward a novel scheme named as BTMPP (which is able to provide both the precise trust management and strong conditional privacy preservation simultaneously) in this article by leveraging the famous bloom filter (BF)-based private set intersection (PSI) technology. Furthermore, the theoretical analysis for the correctness, strong conditional privacy preservation capability, strong robustness, precise trust management, and the other features is detailed, and a series of simulations are conducted. The results reveal that the proposed scheme significantly outperforms the existing schemes in several aspects.

With the advancement of mobile Internet, cloud computing, and smart sensing devices, location-based services (LBSs) have become more and more indispensable in the Internet-of-Things (IoT)-based smart cities. Especially, spatial keyword queries have been widely deployed in real-life applications in recent years. Recently, several privacy-preserving spatial keyword queries schemes were proposed to guarantee data security and query privacy on outsourced data. However, these schemes support neither dynamic update nor diverse query types, which cannot meet the requirements in practical applications. This article proposes two secure dynamic spatial keyword queries (SDSKQs) constructions that support expressive query types and dynamic update. First, we present a basic SDSKQ construction based on hidden-vector encryption and order-revealing encryption. Specifically, we propose a secure hybrid index structure forspatio-textualdata, named encrypted textual signature quadtree (ETSQ-tree). Using ETSQ-tree, the server can prune the index tree according to search queries to reduce the search space. Besides, the ETSQ-tree can be updated dynamically. To resist the file-injection attack, which aims to infer query information according to newly inserted objects, we further improve the basic SDSKQ to achieve forward security. We implement our two constructions and evaluate them using real-world data sets. The experimental results show that they are efficient and feasible in practical applications, and the comparative evaluation confirms that the performance of our constructions outperforms that of the state-of-the-art schemes.

Dynamic Searchable Symmetric Encryption (DSSE) has gained increasing popularity as it enables users to perform both file updates and ciphertext retrieval over encrypted data . However, existing DSSE schemes still lead to privacy leakage ( e.g., forward and backward privacy) in the dynamic setting. Some forward and backward secure DSSE schemes have been proposed, but still cannot support the keyword shielding flexibly. To solve this challenging issue, we propose a Forward and Backward Authorized Keyword Search (FB-AKS) scheme with recoverable keyword shielding by using trapdoor permutations and puncturable encryption in this paper. Compared with existing forward and backward private schemes, FB-AKS achieves keyword authorization flexibly ( e.g., keyword shielding, keyword un-shielding). The formal security analysis proves that FB-AKS achieves forward and backward security. And extensive experiments demonstrate that FB-AKS has less computation and storage overheads .

Ciphertext-Policy Attribute-Based Keyword Search (CP-ABKS) schemes support both fine-grained access control and keyword-based ciphertext retrieval, which make these schemes attractive for resource-constrained users (i.e., mobile or wearable devices, sensor nodes, etc.) to store, share and search encrypted data in the public cloud. However, ciphertext length and decryption overhead in the existing CP-ABKS schemes grow with the complexity of access policies or the number of data users' attributes. Moreover, such schemes generally do not consider the practical multi-owner setting (e.g., each file needs to be signed by multiple data owners before being uploaded to the cloud server) or prevent malicious cloud servers from returning incorrect search results. To overcome these limitations, in this paper we first design an optimized Verifiable Fine-grained Keyword Search scheme in the static Multi-owner setting (termed as basic VFKSM), which achieves short ciphertext length, fast ciphertext transformation, accelerated search process, and authentic search result verification. Then, we extend the basic VFKSM to support multi-keyword search and multi-owner update (also called as extended VFKSM). Finally, we prove that the basic (or extended) VFKSM resists the Chosen-Keyword Attack (CKA) and external Keyword-Guessing Attack (KGA). We also evaluate the performance of these schemes using various public datasets.

Searchable Encryption (SE) is an important technique to guarantee data security and usability in the cloud at the same time. Leveraging Ciphertext-Policy Attribute-Based Encryption (CP-ABE), the Ciphertext-Policy Attribute-Based Keyword Search (CP-ABKS) scheme can achieve keyword-based retrieval and fine-grained access control simultaneously. However, the single attribute authority in existing CP-ABKS schemes is tasked with costly user certificate verification and secret key distribution. In addition, this results in a single-point performance bottleneck in distributed cloud systems. Thus, in this paper, we present a secure Multi-authority CP-ABKS (MABKS) system to address such limitations and minimize the computation and storage burden on resource-limited devices in cloud systems. In addition, the MABKS system is extended to support malicious attribute authority tracing and attribute update. Our rigorous security analysis shows that the MABKS system is selectively secure in both selective-matrix and selective-attribute models. Our experimental results using real-world datasets demonstrate the efficiency and utility of the MABKS system in practical applications.

Ciphertext-Policy Attribute-Based Keyword Search (CP-ABKS) facilitates search queries and supports fine-grained access control over encrypted data in the cloud. However, prior CP-ABKS schemes were designed to support unshared multi-owner setting, and cannot be directly applied in the shared multi-owner setting (where each record is accredited by a fixed number of data owners), without incurring high computational and storage costs. In addition, due to privacy concerns on access policies, most existing schemes are vulnerable to off-line keyword-guessing attacks if the keyword space is of polynomial size. Furthermore, it is difficult to identify malicious users who leak the secret keys when more than one data user has the same subset of attributes. In this paper, we present a privacy-preserving CP-ABKS system with hidden access policy in Shared Multi-owner setting (basic ABKS-SM system), and demonstrate how it is improved to support malicious user tracing (modified ABKS-SM system). We then prove that the proposed ABKS-SM systems achieve selective security and resist off-line keyword-guessing attack in the generic bilinear group model. We also evaluate their performance using real-world datasets.

Structured Encryption (STE), which allows a server to provide secure search services on encrypted data structures, has been widely investigated in recent years. To meet expressive search requirements in practical applications, a large number of STE constructions have been proposed either on textual keywords or spatial data. However, STE on spatio-textual data, which are widely used in location-based services, has not been fully investigated. In this paper, we formally define the notion of Spatial Keyword Structured Encryption (SKSE) and propose several concrete SKSE constructions with various efficiency-security trade-offs. Firstly, we propose a basic construction with linear search complexity, which only leaks the private files matching both spatial range query and all query keywords. Then, to improve the search efficiency on large-scale datasets, we present a novel tree-based construction with sub-linear search complexity. Finally, we introduce a post-validation approach to remove false positives and further improve storage and search performance. Our constructions are general in the sense that they can be constructed from any hidden vector encryption schemes, including public-key setting and symmetric-key setting, which can meet different sharing requirements. Our rigorous security analysis and comprehensive performance evaluation demonstrate that the proposed constructions are secure and outperform the start-of-the-art solutions.

With the development of outsourcing services, users with limited resources tend to store encrypted images on remote servers and search them anytime and anywhere. However, existing encrypted image search schemes are proposed for cloud computing scenarios, and have some defects, such as excessive bandwidth resource consumption or network delay, which are not suitable for Internet of Things (IoT) devices in edge computing environment. Therefore, we propose a secure and verifiable multikey image search (SVMIS) scheme in cloud-assisted edge computing. First, the pretrained convolutional neural network model is employed to extract image feature vectors to improve search accuracy. Then, a key distribution protocol is designed to convert the encrypted indexes of different owners, and a transformation key list is constructed to support the multikey setting in edge computing. Next, the learning with errors based secure k-nearest neighbor algorithm is used to encrypt feature vectors to improve security. Finally, the Merkle hash tree is utilized to check the correctness of search results returned by edge servers. Theoretical analysis and extensive experiments using a real-world dataset evaluate the security and effectiveness of SVMIS.

Nowadays, collaborative learning is becoming a new trend to address the data scarcity issue. To prevent potential privacy leakage, some privacy-preservation collaborative learning schemes have been proposed with data encryption, but cannot handle the setting of different data contribution among data nodes and avoid the huge overhead of implementing over encrypted data. In this paper, we design a flexible and secure decentralized collaborative learning to achieve the contribution over data nodes, where each data node can specialize the contribution extent for collaborative learning. Besides, we provide a MPC-friendly collaborative layer for the lightweight privacy preservation. Our security analysis and experimental results demonstrate the security and superiority of our system, respectively.

With the increasing popularity of geo-positioning technologies and mobile Internet, spatial keyword data services have attracted growing interest from both the industrial and academic communities in recent years. Meanwhile, a massive amount of data is increasingly being outsourced to cloud in the encrypted form for enjoying the advantages of cloud computing while without compromising data privacy. Most existing works primarily focus on the privacy-preserving schemes for either spatial or keyword queries, and they cannot be directly applied to solve the spatial keyword query problem over encrypted data. In this paper, we study the challenging problem of Privacy-preserving Boolean Range Query (PBRQ) over encrypted spatial databases. In particular, we propose two novel PBRQ schemes. Firstly, we present a scheme with linear search complexity based on the space-filling curve code and Symmetric-key Hidden Vector Encryption (SHVE). Then, we use tree structures to achieve faster-than-linear search complexity. Thorough security analysis shows that data security and query privacy can be guaranteed during the query process. Experimental results using real-world datasets show that the proposed schemes are efficient and feasible for practical applications, which is at least ×70 faster than existing techniques in the literature.

Searchable encryption (SE) has been a promising technology which allows users to perform search queries over encrypted data. However, the most of existing SE schemes cannot deal with the shared records that have hierarchical structures. In this paper, we devise a basic cryptographic primitive called as attribute-based keyword search over hierarchical data (ABKS-HD) scheme by using the ciphertext-policy attribute-based encryption (CP-ABE) technique, but this basic scheme cannot satisfy all the desirable requirements of cloud systems. The facts that the single keyword search will yield many irrelevant search results and the revoked users can access the unauthorized data with the old or outdated secret keys make this basic scheme not scale well in practice. To this end, we also propose two improved schemes (ABKS-HD-I, ABKS-HD-II) for the sake of supporting multi-keyword search and user revocation, respectively. In contrast with the state-of-the-art attribute-based keyword search (ABKS) schemes, the computation overhead of our schemes almost linearly increases with the number of users' attributes rather than the number of attributes in systems. Formal security analysis proves that our schemes are secure against both chosen-plaintext attack (CPA) and chosen-keyword attack (CKA) in the random oracle model. Furthermore, empirical study using a real-world dataset shows that our schemes are feasible and efficient in practical applications.

With the advances of outsourced computation, the threats of data leakage and loss of computational accuracy over rational domain are attracting increasing concerns. In this paper, we propose a framework for Privacy-preserving Data Sharing and high-accurate outsourced Computation system, referred as PDSC. PDSC system can perform secure data sharing with multiple data providers. Besides, the original data and computed results in the rational field can be securely processed and stored in the cloud without privacy leakage. Specifically, we design privacy-preserving computation protocols over rational numbers to guarantee computational accuracy and handle outsourced operations on-the-fly. Detailed security analysis and experimental results demonstrate that PDSC system is secure and feasible, respectively.

The traditional privacy-preserving image retrieval schemes not only bring large computational and communication overhead but also cannot well protect the image and query privacy in multi-user scenarios. To solve the above problems, we first propose a basic privacy-preserving content-based image retrieval (CBIR) framework which significantly reduces storage and communication overhead compared with the previous works. Furthermore, we design a new efficient key conversion protocol to support unshared key multi-owner multi-user image retrieval without losing search precision. Moreover, our framework supports unbounded attributes and can trace malicious users according to leaked secret keys, which significantly improve the usability of multi-source data sharing. Strict security analysis shows that the user privacy and outsourced data security can be guaranteed during the image retrieval process, and the performance analysis using real-world dataset shows that the proposed image retrieval framework is efficient and feasible for practical applications.

An aggregate signature (AS) scheme combines multiple signatures which is generated by many different users into a single one. This feature is very beneficial for diminishing storage cost, bandwidth and verification cost. Many previous attempts have been made for designing AS schemes, while the former security models have not clearly addressed coalition attacks, and most of the existing AS schemes cannot resist these kinds of attacks. In this study, the authors provide a modified security model of certificateless AS (CLAS) schemes and then give a new CLAS scheme. The security of their present scheme can be rigorously proved based on the computational Diffie–Hellman assumption in the random oracle model. Furthermore, their scheme can resist such coalition attacks, i.e. an AS in their scheme is valid iff all single signatures used to generate the AS are valid.

Cloud-assisted Internet of Things (IoT) is increasingly prevalent in our society, for example in home and office environment; hence, it is also known as cloud-assisted Internet of Everything (IoE). While in such a setup, data can be easily shared and disseminated (e.g., between a device, such as Amazon Echo and the cloud, such as Amazon AWS), there are potential security considerations that need to be addressed. Thus, a number of security solutions have been proposed. For example, searchable encryption (SE) has been extensively studied due to its capability to facilitate searching of encrypted data. However, threat models in most existing SE solutions rarely consider the malicious data owner and semi-trusted cloud server at the same time, particularly in dynamic applications. In a real-world deployment, disputes between above two parties may arise as either party will accuse the other of some misbehavior. Furthermore, efficient full-update operations (e.g., data modification, data insertion, and data deletion) are not typically supported in the cloud-assisted IoE deployment. Therefore, in this paper, we present a fair and dynamic data sharing framework (FairDynDSF) in the multiowner setting. Using FairDynDSF, one can check the correctness of search results, achieve fair arbitration, multikeyword search, and dynamic update. We also prove that FairDynDSF is secure against inside keyword guessing attack and demonstrate its efficiency by evaluating its performance using various datasets.

Ciphertext-policy attribute-based keyword search (CP-ABKS) schemes facilitate the fine-grained keyword search over encrypted data, such as those sensed/collected from Industrial Internet of Things (IIoT) devices and stored in the cloud. However, existing CP-ABKS schemes generally have significant computation and storage requirements, which are beyond those of resource-constrained IIoT devices. Therefore, in this paper, we design a secure online/offline data sharing framework (DSF), which supports online/offline encryption and outsourced decryption. Using the healthcare setting as a case study, we demonstrate how DSF can be deployed in the cloud-assisted Healthcare IIoT (HealthIIoT) system. We not only prove that the DSF is selectively secure in the chosen access structure security model but also demonstrate its efficiency and feasibility in practical scenarios using experiments.

Public-key encryption with keyword search (PEKS) enables users to search over encrypted data and retrieve target data efficiently. However, most of existing PEKS schemes are vulnerable to adversaries equipped with quantum computers in the near future, and even incur complex certificate management procedures due to the public key infrastructure (PKI). To this end, we propose a proxy-oriented identity-based encryption with keyword search (PO-IBEKS) scheme from lattices for cloud storage, which is post-quantum secure. In PO-IBEKS, an original data owner authorizes a proxy to encrypt sensitive data as well as corresponding keywords and upload ciphertexts to clouds, which alleviates the data processing burden on the original data owner. Besides, PO-IBEKS can resist inside keyword guessing attacks (IKGA) from misbehaved cloud servers by integrating the learning with errors (LWE) encryption and preimage sampleable function. Each entity in PO-IBEKS is identified with her/his recognizable information, thereby eliminating managing certificates. Formal security analysis proves that PO-IBEKS can achieve ciphertext indistinguishability, existential unforgeability, and delegation security. Experimental results demonstrate PO-IBEKS is much more practical when compared with existing schemes.

Equipped with the emerging cloud computing, clients prefer to outsource the increasing number of Industrial Internet of things (IIoT) data to cloud to reduce the high storage and computation burden. However, existing searchable encryption (SE) schemes just apply to IIoT records containing textual keyword fields rather than both digital and textual keyword ones. Besides, the key management issue still impedes the practicality and availability of SE schemes due to high key storage overhead. To this end, we present an outsourced Hybrid Keyword-Field Search over encrypted data with efficient Keys Management (HKFS-KM) scheme by utilizing the relevance score function and keyed hash tree. Formal security analysis proves that the HKFS-KM scheme can achieve keyword privacy and trapdoor unlinkability in both known ciphertexts attack model and known background attack model. Experimental results using real-world dataset show its efficiency and practicality in practice.

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.

With the development of Mobile Healthcare Monitoring Network (MHMN), patients' personal data collected by body sensors not only allows patients to monitor their health or make online pre-diagnosis but also enable clinicians to make proper decisions by utilizing data mining technique. However, the sensitive data privacy is still a major concern. In this paper, we first propose an Efficient Privacy-preserving Sensor data Monitoring and online Diagnosis (EPSMD) system for outsourced computing, then furnish an improved Multidimensional Range Query Technique (MRQT) to gain a broad range of applications in practice. In addition, a privacy-preserving naive Bayesian classifier based on MRQT is designed to protect patients' data in data mining and online diagnosis efficiently. Security analysis proves that patients' data privacy can be well protected without loss of data confidentiality, and performance evaluation demonstrates the efficiency and accuracy in data monitoring and disease pre-diagnosis, respectively.

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 fast development of Logistics Internet of Things and smart devices, the security of express information processed by mobile devices in Logistics Internet of Things has attracted much attention. However, the existing secure express schemes only focus on privacy protection of personal information but do not consider the security of the logistics information against couriers with malicious mobile devices. For example, a privacy‐preserving delivery path should be required in order to prevent the privacy leakage in the express delivery procedure. Therefore, besides the security of personal information, the privacy protection of logistics information and authentication of mobile devices used in express company are important to security in Logistics Internet of Things. In this paper, we propose a secure logistics information scheme LIP‐PA to provide privacy protection of both personal information and logistics information. First, we define the basic requirements of Logistics Internet of Things. Then, using attribute‐based encryption and position‐based key exchange, we propose a logistics information privacy protection scheme with position and attribute‐based access control for mobile devices. The analysis results show that our scheme satisfies the defined requirements. Finally, the performance of our scheme is evaluated and the experiment results show that our scheme is efficient and feasible for mobile devices in real parcel delivery scenario.

User authentication has been widely deployed to prevent unauthorized access in the new era of Internet of Everything (IOE). When user passes the legal authentication, he/she can do series of operations in database. We mainly concern issues of data security and comparable queries over ciphertexts in IOE. In traditional database, a Short Comparable Encryption (SCE) scheme has been widely used by authorized users to conduct comparable queries over ciphertexts, but existing SCE schemes still incur high storage and computational overhead as well as economic burden. In this paper, we first propose a basic Short Comparable Encryption scheme based on sliding window method (SCESW), which can significantly reduce computational and storage burden as well as enhance work efficiency. Unfortunately, as the cloud service provider is a semitrusted third party, public auditing mechanism needs to be furnished to protect data integrity. To further protect data integrity and reduce management overhead, we present an enhanced SCESW scheme based on position-aware Merkle tree, namely, PT-SCESW. Security analysis proves that PT-SCESW and SCESW schemes can guarantee completeness and weak indistinguishability in standard model. Performance evaluation indicates that PT-SCESW scheme is efficient and feasible in practical applications, especially for smarter and smaller computing devices in IOE.

Summary With powerful storage and computing capacities provided by cloud server provider(CSP), cloud customers can relieve from heavy storage and maintenance burden in cloud computing. Therefore, searchable encryption(SE) technology becomes a fundamental solution to search over encrypted data in outsourcing service. However, the genuine safety of SE schemes should concentrate not only on keyword privacy but also on user privacy as information tracking may leak user identity. For example, in the personal health record system, the malicious CSP may match sensitive disease information(cancer or AIDS) with certain patient. In addition, practical SE scheme should not be confined to single‐user setting because of its limitations. While SE schemes applied to multi‐user setting may result in additional secret key and ciphertext updating burden due to frequent user revocation. Along this direction, we define a revocable and anonymous SE scheme in multiple‐user setting, which is scalable and efficient in user revocation and anonymity. Security analysis shows that our scheme is Anonymous‐Revocable‐ID‐CPA secure under Decision Bilinear Diffie–Hellman assumption and is able to effectively resist decryption key exposure threat. Copyright © 2015 John Wiley & Sons, Ltd.

With the emergence of cloud storage, enabling cloud clients to securely store and efficiently retrieve ciphertext is a fundamental issue in cloud computing as data outsourcing can greatly ease heavy computation and management burden locally. Unfortunately, two challenging issues (i.e., data security and privacy) dramatically impede the adaption and practicability of cloud storage due to honest-but-curious cloud service provider (CSP). Furthermore, data encryption mechanism seriously makes the information retrieval over ciphertext extremely difficult. Besides, dealing with single CSP is predicted to become less popular with cloud customers for fear of risks of single-point failure threats and potential malicious insiders. To this end, we propose two efficient keyword search over encrypted data in multi-cloud setting schemes which exploit Identity-Based Encryption (IBE) and Key-Policy Attribute-Based Encryption (KP-ABE), respectively. Formal security analysis proves that our schemes can guarantee data privacy and reliability. As a further contribution, experimental results over real-world dataset show that our proposed schemes are feasible and efficient in practical applications. Copyright © 2016 John Wiley & Sons, Ltd.