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.

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

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.

Different forms of cybercrimes, ranging from email spam and click fraud to the most sophisticated underground economy, have been studied extensively. Fraud and misconduct in legitimate Internet economy, however, have not received sufficient attention, even though potential damages may not be as severe as regular cybercrimes. In this paper, we have performed the first in-depth empirical investigation of fraud and misconduct in legitimate Internet businesses by collecting 6.6 million customer complaints, which were filed over 45 months by 2.7 million customers against 117 thousand merchants on one of the largest customer complaint platforms in the world, along with more than 13 million customer-uploaded images as photographic evidence. We characterized the complaints in terms of merchants, main issues, desired remedy, amount of money involved, customer ratings, and etc. Most importantly, we were able to uncover various fraud and misconduct in different Internet businesses, some of which should have caught law enforcement's attention. The sum of the amount of money involved in these complaints is 5.4 billion US dollars. We also investigated the privacy inference of the images, and found that the image content could disclose an unexpected amount of customers' personal information.

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.