The development of E-commerce platforms has provided convenience for consumers, and many shopping platforms advertise products to users based on their preferences. However, this kind of recommendation, based on historical browsing or purchase data, can infer user behavior, which threatens user privacy. Encrypted browsing or purchase data protects user privacy but blinds the server from making recommendations. We have improved the algorithm (GPKE-MET) based on equivalence testing for public key encryption to support batch testing, which can be applied to large-scale encrypted data processing on E-commerce platforms. In addition,GPKE-METis against guessing by the attackers (i.e., malicious cloud server) for the predictable information so as to enhance the data privacy. Therefore, with the help ofGPKE-MET, the E-commerce platform can make product recommendations effectively, although the underlying data is encrypted.

Personalized federated learning (pFL) has emerged as a promising paradigm for mitigating client heterogeneity in distributed machine learning. In cross-device scenarios, however, the continuous generation of sensitive data by clients introduces severe communication bottlenecks and privacy risks, limiting the effectiveness of existing pFL methods. To ad dress these challenges, we propose Fed-EHP, a novel privacy preserving and communication-efficient framework for hetero geneous pFL. The originality of Fed-EHP lies in its task-specific synergistic integration of three customized components within a unified fog-assisted architecture: 1) Data-aware client clustering at the fog layer to alleviate statistical heterogeneity and reduce communication load; 2) MIFE-based secure aggregation to ensure strong privacy protection against inference attacks while preserving model utility; and 3) Cluster-driven personalized knowledge distillation to effectively address model heterogeneity and boost personalization across devices and fog nodes. To demonstrate privacy guarantee and security of the proposed framework, we provide a formal security analysis. We also con duct extensive experiments on MNIST, Fashion-MNIST, CIFAR 10, and CIFAR-100. Fed-EHP consistently delivers notable im provements in both accuracy and communication efficiency over state-of-the-art pFL methods. These results demonstrate that our integrated and customized framework enables capabilities and performance gains that are unattainable using existing techniques in isolation, establishing Fed-EHP as a practical and reliable solution for real-world heterogeneous federated learning.

Privacy-preserving similarity retrieval for ciphertext images has broad applications in Internet of Things (IoT) areas, including smart healthcare, face recognition, and social networking. However, most existing privacy-preserving schemes suffer from inefficient retrieval and limited security guarantees due to the use of unreasonable encryption methods. In addition, those supporting fine-grained access control often lack scalability or are computationally inefficient. To address these challenges, we propose an efficient similarity retrieval scheme for ciphertext images that ensures both privacy preservation and fine-grained access control. First, we construct an encrypted index tree using clustering to improve retrieval efficiency while preserving high recall. Second, we achieve security against chosen-plaintext attacks (CPA) and result verification by employing symmetric homomorphic encryption and Merkle hash tree. Third, we realize access control for each image, enabling simultaneous access verification and similarity retrieval via a single inner product operation. Our theoretical and experimental analysis shows that the proposed scheme is CPA-secure and achieves up to 100× faster query processing than existing CPA-secure schemes, with the ability to retrieve 2000 ciphertext images within 0.5 seconds for 128-dimensional feature vectors.

Secure deduplication not only optimizes cloud storage but also prevents data leakage. However, traditional schemes are with high computation and communication costs to deal with large-scale multimedia data. To address this problem, we propose a fog computing-based fuzzy deduplication (HDeFC), a two-stage privacy-preserving deduplication protocol. Our approach prevents guessing attacks and single point of failure, besides that also implements noninteractive Proof of Ownership and label consistency verification. In addition, we also achieve the access permission for only legitimate users. Our performance evaluation shows that the hierarchical setting is efficient in deduplication.

Cloud computing eliminates the limitations of local hardware architecture while also enabling rapid data sharing between healthcare institutions. Encryption of electronic medical records (EMRs) before uploading to cloud servers is necessary for privacy. However, encryption brings challenges for computation. Public Key Encryption with Equality Test (PKEET) allows cloud servers to test the underlying message equality without decryption. Therefore, it can be used to classify the encrypted EMRs corresponding to different medical symptoms. However, traditional PKEETs have limitations in testing the similarity between the ciphertexts. Undoubtedly, it can not handle EMR classification with similar medical symptoms efficiently. In this work, we propose a lightweight public key encryption with similarity test (PKEST) for the EMR classification shared in medical consortia. Our scheme can resist offline message recovery attacks, which may be launched by the insider manager, and the traditional paring computation is not necessary. Our experiment simulation shows that the similarity error between ciphertext and plaintext is tiny when the parameters are set properly. Compared to previous works, our scheme not only achieves the classification of similar encrypted EMRs but is also more efficient than traditional PKEETs since our construction does not need paring computation anymore.

Huge amounts of data from various sources are substantial to dependable distributed machine learning, especially for trustworthy federated learning (FL). However, existing FL methods are difficult to collect enough data for training the global model more accurately, especially in cross-device scenarios. In this paper, we propose a new federated generative adversarial network empowered by differential privacy and knowledge transfer named Fed-GAN, which can be used to address the problem of data shortage and prevent generator leakage from resource-constrained devices, as well as generate high-quality synthetic data while ensuring strict DP guarantees. Different from other generative model methods, our Fed-GAN framework can achieve efficient and secure generative model training and limited permission for resource-constrained devices to prevent them from leaking or misusing the generator. In addition, we propose a pHash-KT method for our Fed-GAN framework, which selects potentially high-quality data through the knowledge of each client for improving the utility of synthetic data. Our FedGAN framework satisfies ($\frac{2kJ\lambda}{\sigma^2}+\frac{log 1/\delta}{\lambda-1},\delta}$)-DP, and also has high resistance when number of adversaries is 10%-70% of the total number of clients. Extensive experiments demonstrate that our Fed-GAN framework not only generates high-quality synthetic data, but also provides strict DP guarantees, compared with other generative model methods. Our code is publicly available at https://github.com/daxx1/fed-gan

Fuzzy deduplication frees up more storage space than exact deduplication. Traditional deduplication schemes do not only fail to extend to fuzzy deduplication directly but also be vulnerable to brute-force guessing attacks (e.g. Convergent Encryption (CE) and Message-locked Encryption (MLE)). Currently, fuzzy deduplication is mainly handled with the help of aided-server and a third-party validator for the security and feasibility. This work extracts a single-server fuzzy deduplication scheme for encrypted multimedia data under dual-entity (e.g., uploader & server, and a third-party is not necessary). In addition, we perform experimental evaluation of DEFD on real-world datasets. The results show that DEFD can save 95+% of storage space, and for the real outsourced data deduplication scenarios DEFD can improve the accuracy by 5.287%.

The advanced metering infrastructure (AMI) network allows control center (CC) to collect residential users’ fine-grained electricity usage data every few minutes for energy management and real-time load monitoring. However, these fine-grained data may reveal users’ daily activities which raises serious privacy concerns. For allowing the CC to receive only the total electricity usage of users while preserve their privacy, many privacy-preserving data aggregation (PPDA) schemes have been put forward. Nevertheless, most of them have no regard for privacy-preserving multisubset data aggregation (PPMDA), where the CC not only needs to learn the number of users whose electricity usage lies within a given range but also the overall electricity usage of these users. Moreover, to the best of our knowledge, there is no formal study on achieving multifunctional PPMDA for AMI networks. In this article, we come up with a multifunctional, flexible, privacy-enhanced, and efficient PPMDA scheme, named MPP-MDA. In our MPP-MDA, the CC can compute multiple statistical function aggregations of each subset of users to provide various fine-grained services. In addition, for better flexibility, MPP-MDA supports billing of dynamic pricing, achieves fault tolerance and adapts to dynamic users. Moreover, MPP-MDA preserves differential privacy against differential attack, guarantees authentication and data integrity. Finally, MPP-MDA supports privacy-preserving fivefold-functional aggregation, which is able to reduce the computation and communication overheads significantly. The security discussion elaborates that MPP-MDA is secure against many attacks. The performance evaluation demonstrates that MPP-MDA has less computation and communication overheads.

The smart grid (SG) is a new type of grid that integrates traditional power grid with the Internet of Things (IoT) to make the entire grid system more compatible, controllable and self-healing. However, the flourishing of SG still faces some challenges in term of privacy-preserving data aggregation. Previous multi-dimensional data aggregation schemes need heavy computation operations, cannot support multi-subset data aggregation, and resist neither collusion attack among the gateway (GW) and control center (CC) nor differential attack. To solve these issues, we propose a privacy-preserving data aggregation scheme for fog-based smart grids to achieve multi-dimensional and multi-subset data aggregation. The parallel composability of differential privacy is used to reasonably allocate the privacy budget, which can provide higher data utility in multi-dimensional data aggregation. In addition, each user’s multi-dimensional power consumption data will be structured as a composite data by utilizing Chinese Remainder Theorem (CRT), which will further reduce the computational overhead. Security analysis shows that our scheme can resist differential attack, eavesdropping attack, collusion attack and active attack. Evaluation of the performance also demonstrates that our scheme is more efficient in terms of computational overhead and communication overhead.

Privacy-preserving federated learning, as one of the privacy-preserving computation techniques, is a promising distributed and privacy-preserving machine learning (ML) approach for Internet of Medical Things (IoMT), due to its ability to train a regression model without collecting raw data of data owners (DOs). However, traditional interactive federated regression training (IFRT) schemes rely on multiple rounds of communication to train a global model and are still under various privacy and security threats. To overcome these problems, several noninteractive federated regression training (NFRT) schemes have been proposed and applied in a variety of scenarios. However, there are still several challenges: 1) how to protect the privacy of DOs' local dataset; 2) how to realize highly scalable regression training without linear dependence on sample dimension; 3) how to tolerate DOs' dropout; and 4) how to enable DOs to verify the correctness of aggregated results returned from the cloud service provider (CSP). In this article, we propose two practical noninteractive federated learning schemes with privacy-preserving for IoMT, named homomorphic encryption based NFRT (HE-NFRT) and double-masking protocol based NFRT (Mask-NFRT), respectively, which are based on a comprehensive consideration of NFRT, privacy concerns, high-efficiency, robustness, and verification mechanism. The security analyses display that our proposed schemes are able to protect the privacy of DOs' local training data, resist collusion attack, and support strong verification to each DO. The performance evaluation results demonstrate that our proposed HE-NFRT scheme is desirable for a high-dimensional and high-security IoMT application while Mask-NFRT scheme is desirable for a high-dimensional and large-scale IoMT application.

Cloud storage technology is constantly evolving, resulting in a significant amount of duplicate data being stored in the cloud, particularly multimedia data such as images and videos. In terms of data privacy and storage optimization, the encrypted deduplication should be checked to save space overhead for cloud servers. Compared to exact deduplication, fuzzy deduplication is low-cost for encrypted multimedia data. Focusing on reducing the false deletion rate, an efficient and secure fuzzy deduplication system based on dual-feature without additional servers is proposed. We also propose a concept of pre-verification for label consistency to compensate for the loss that cannot be fixed through post-verification. Therefore, it is more practical. Finally, we conduct experiments on real-world datasets for performance evaluation. The experimental results show good performance in terms of both computational cost and deduplication efficiency.

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

Data privacy, especially location privacy, is paramountly important for protecting individual's information in smart cities in the big data era. One of the examples is in spatial crowdsourcing (SC). It enables people not only to issue spatiotemporal tasks to ask for help as requesters but also to solve others' tasks as workers on the SC platform. While SC brings convenience to people, it also produces severe location privacy problems, which have been recently paid more attention from both academia and industries. In this article, we address the location privacy problem in SC in a practical and secure way. We propose a location privacy-preserving framework for almost all existed mainstream distance computations in the SC system, namely, Euclidean-L3P, Minkowski-L3P, Manhattan-L3P, and Chebyshev-L3P, among which the first two are constructed based on homomorphic encryption and composite-order multilinear mapping while the latter two on the homomorphic encryption and prefix membership verification approach. Location privacy is resolved because of the above techniques having enabled that all distance computations are evaluated through ciphertexts without disclosing any location information. Security analysis shows that our framework can prevent a strong adversary from obtaining participants' location privacy. Performance analysis evaluates computation and communication overheads between protocols. The results show that Euclidean-L3P is more efficient than Manhattan-L3P and Chebyshev-L3P in terms of computation overheads when the SC applications require a small number of participants, a large plaintext space, and a small number of base stations. Moreover, compared with Manhattan-L3P and Chebyshev-L3P, Euclidean-L3P is a better choice in terms of communication overhead.

Wireless body area networks (WBANs), as a promising health-care system, can provide tremendous benefits for timely and continuous patient care and remote health monitoring. Owing to the restriction of communication, computation and power in WBANs, cloud-assisted WBANs, which offer more reliable, intelligent, and timely health-care services for mobile users and patients, are receiving increasing attention. However, how to aggregate the health data multifunctionally and efficiently is still an open issue to the cloud server (CS). In this paper, we propose a privacy-preserving and multifunctional health data aggregation (PPM-HDA) mechanism with fault tolerance for cloud-assisted WBANs. With PPM-HDA, the CS can compute multiple statistical functions of users' health data in a privacy-preserving way to offer various services. In particular, we first propose a multifunctional health data additive aggregation scheme (MHDA+) to support additive aggregate functions, such as average and variance. Then, we put forward MHDA⊕as an extension of MHDA+to support nonadditive aggregations, such as min/max, median, percentile, and histogram. The PPM-HDA can resist differential attacks, which most existing data aggregation schemes suffer from. The security analysis shows that the PPM-HDA can protect users' privacy against many threats. Performance evaluations illustrate that the computational overhead of MHDA+is significantly reduced with the assistance of CSs. Our MHDA⊕scheme is more efficient than previously reported min/max aggregation schemes in terms of communication overhead when the applications require large plaintext space and highly accurate data.

Online anomaly detection (AD) is an important technique for monitoring wireless sensor networks (WSNs), which protects WSNs from cyberattacks and random faults. As a scalable and parameter-free unsupervised AD technique, $(k)$-nearest neighbor (kNN) algorithm has attracted a lot of attention for its applications in computer networks and WSNs. However, the nature of lazy-learning makes the kNN-based AD schemes difficult to be used in an online manner, especially when communication cost is constrained. In this paper, a new kNN-based AD scheme based on hypergrid intuition is proposed for WSN applications to overcome the lazy-learning problem. Through redefining anomaly from a hypersphere detection region (DR) to a hypercube DR, the computational complexity is reduced significantly. At the same time, an attached coefficient is used to convert a hypergrid structure into a positive coordinate space in order to retain the redundancy for online update and tailor for bit operation. In addition, distributed computing is taken into account, and position of the hypercube is encoded by a few bits only using the bit operation. As a result, the new scheme is able to work successfully in any environment without human interventions. Finally, the experiments with a real WSN data set demonstrate that the proposed scheme is effective and robust.