Federated Learning (FL) enables collaborative model training across untrusted devices while preserving data privacy. However, malicious participants can still launch attacks, especially poisoning attacks, during model aggregation. To defend against poisoning attacks, we propose a Coulomb Force-based Federated Learning (CFFL), a physics-inspired defense framework that integrates neighborhood Coulomb force into FL security mechanisms. CFFL effectively addresses detection failures in poisoning attacks arising from the “distance concentration” among high-dimensional data. Specifically, firstly, we establishes a local update model for clients, where the similarity between model updates is quantified through local Coulomb resultant force (LCRF), effectively distinguishing malicious and benign updates; Secondly, we develop ak-nearest neighbor (KNN)-based Coulomb resultant force anomaly detection (NCFAD) model that identifies malicious updates by isolating top-koutliers with largest neighborhood factor; Finally, experiment results validate that CFFL outperforms state-of-the-art (SOTA) baselines in defense performance and achieves model accuracy equivalent to the baseline FedAvg on NCI-1, PROTEINS_full, and MNIST datasets (90.82%, 92.23%, and 96.49%, respectively). This indicates that CFFL effectively mitigates the impact of poisoning attacks without compromising the benign performance of the aggregation model.

The rapid development of the Artificial Intelligence of Things (AIoT) opens up a new perspective for emerging service-based applications and becomes a major driver of diverse federated learning (FL) applications. However, due to the heterogeneity of nodes and the existence of free-rider attacks, some device nodes may launch free-rider attacks to obtain the global model without any contribution, which not only dampens the enthusiasm of legitimate participants but also undermines the fairness of node contributions. In this article, we propose a free-rider attack detection (FRAD) mechanism for FL with deep autoencoding Gaussian mixture model (DAGMM) based on contribution and reputation. Specifically, we first model the contribution values based on the computing resource, communication cost, and data quality of each device node. Then, based on PageRank algorithms, we design an optimal reputation-based model to fairly and precisely choose benign nodes to participate in federated training under information asymmetry. Furthermore, we develop the FRAD mechanism via DAGMM that combines historical contribution with reputation scores. Simulation results validate that the proposed mechanism in this article outperforms the state-of-the-art baselines$(\sim \!\! 2.14\times $and$\sim \!\! 1.1\times $on MNIST and CIFAR, respectively) in defending against free-rider attacks, and when the main clients are free-riders, i.e., 50% or even up to 80% of the free-riders, FRAD can still maintain a high defense performance against free-rider attacks.

Abstract The wide application of Internet of Things technology has promoted the application of location‐based services (LBS). Users can enjoy various conveniences brought by LBS. However, mobile users should submit their location information to LBS which may lead to privacy disclosure. Therefore, users need to protect their privacy while enjoying the service. If the privacy protection problem cannot be solved, the development of mobile internet business will be greatly affected. The existing location privacy protection methods take little account of real‐time road conditions and are unable to resist link attacks. In this article, a k‐anonymous location privacy protection method based on Voronoi map was proposed. First, the Voronoi diagram was divided according to road network structure and real‐time road network data, and Voronoi unit was established with the intersection as the base point. Then, the base point is sent as an anchor instead of the user's actual location to the third‐party server. The Voronoi diagram generated by the k‐anonymity constituted an anonymous space to resist multi‐query attacks and protected the location. k‐1 user location points that are communicating were selected to form k‐anonymous set with the current user, and send the anonymous set to LBS. Theoretical analysis and experimental results show that the proposed method not only efficiently protected location privacy, but also provided high‐quality service.

Cloud storage can provide a way to effectively store and manage big data. However, due to the separation of data ownership and management, it is difficult for users to check the integrity of data in a traditional way, which leads to the introduction of the auditing techniques. This paper proposes a public auditing protocol with a self-certified public key system using blockchain technology. The user's operational information and metadata information of the file are formed to a block after verified by the checked nodes and then to be put into the blockchain. The chain structure of the block ensures the security of auditing data source. The security analysis shows that attackers can neither derive user’s secret key nor derive users’ data from the collected auditing information in the presented scheme. Furthermore, it can effectively resist against not only the signature forging attacks but also the proof forging attacks. Compared with other public auditing schemes, our scheme based on the self-certified public key system has been improved in storage overhead, communication bandwidth, and verification efficiency.

Location‐based services (LBS) have become an important research area with the rapid development of mobile Internet technology, GPS positioning technology, and the widespread application of smart phones and social networks. LBS can provide convenience and flexibility for the users’ daily life, but at the same time, it also brings security risks to the users’ privacy. Untrusted or malicious LBS servers can collect users’ location data through various ways and disclose it to the third party, thus causing users’ privacy leakage. In this paper, a differential privacy location protection method based on the Markov model for user’s location privacy is proposed. Firstly, the transition probability matrix between states of the n‐order Markov model is used to predict the occurrence state and development trend of events; thereby, the user’s location is predicted, and then a location prediction algorithm based on the Markov model (LPAM) is proposed. Secondly, a location protection algorithm based on differential privacy (LPADP) is proposed, in which location privacy tree (LPT) is constructed according to the location data and the difficulty of retrieval, the two nodes with the largest predicted value of LPT are allocated with a reasonable privacy budget, and Laplace noise is added to protect location privacy. Theoretical analysis and experimental results show that the proposed method not only meets the requirements of differential privacy and protects location privacy effectively but also has high data availability and low time complexity.