Abstract Trajectory data offers significant potential for personalized services and behavioral analysis, but also raises substantial privacy concerns. However, centralized privacy protection strategies are susceptible to single-point failures, often fail to accommodate user-specific privacy needs, and can lack precision in local privacy protection. To address these concerns, this work utilizes the decentralized philosophy of Web 3.0. A novel strategy is proposed, the Blackhole Model-based Distributed Trajectory Privacy-Preserving Strategy (BlackHole-DTP). First, the Distributed Deep Learning Trajectory Training Algorithm with Multi-head Attention and Variational Adversarial Autoencoders is introduced to improve the simulation of temporal and semantic information in trajectory data, thereby enhancing data utility and accuracy. Additionally, a Local Differential Privacy Algorithm based on the Data Blackhole is designed that dynamically adjusts privacy protection levels according to user requirements. This algorithm incorporates principles inspired by general relativity to determine perturbation values. Finally, experimental results demonstrate that BlackHole-DTP provides superior privacy protection, achieving significantly lower success rates in reconstruction and re-identification attacks compared to baseline models. Specifically, under high-precision requirements, the attack success rate is reduced by $\sim $30%.

Abstract Location data management plays a crucial role in facilitating data collection and supporting location-based services. However, the escalating volume of transportation big data has given rise to increased concerns regarding privacy and security issues in data management, potentially posing threats to the lives and property of users. At present, there are two possible attacks in data management, namely Reverse-clustering Inference Attack and Mobile-spatiotemporal Feature Inference Attack. Additionally, the dynamic allocation of privacy budgets emerges as an NP-hard problem. To protect data privacy and maintain utility in data management, a novel protection model for location privacy information in data management, Classified Regional Location Privacy-Protection Model based on Personalized Clustering with Differential Privacy (PCDP-CRLPPM), is proposed. Firstly, a twice-clustering algorithm combined with gridding is proposed, which divides continuous locations into different clusters based on the different privacy protection needs of different users. Subsequently, these clusters are categorized into different spatiotemporal feature regions. Then, a Sensitive-priority algorithm is proposed to allocate privacy budgets adaptively for each region. Finally, a Regional-fuzzy algorithm is presented to introduce Laplacian noise into the centroids of the regions, thereby safeguarding users’ location privacy. The experimental results demonstrate that, compared to other models, PCDP-CRLPPM exhibits superior resistance against two specific attack models and achieves high levels of data utility while preserving privacy effectively.

Federated Learning (FL) is the standard paradigm for privacy-preserving model training across distributed Industrial IoT (IIoT) devices; however, deployment remains hindered by non-IID data, high communication costs, and unstable asynchronous convergence. We present Energy-Aware Asynchronous Federated Learning with Adaptive Layered Personalization (EAFL-ALP), which achieves a 99.8% reduction in per-round traffic while improving accuracy and robustness. The framework comprises three coordinated modules: (1) Adaptive Fractal-Wave Personalisation Model (AFWPM), which for each client, grows an entropy-conditioned fractal branch and prunes it with wave-collapse, yielding a self-similar, capacity-adaptive head that captures data heterogeneity; (2) Layerwise Quantization-Based Reversible Differential Privacy Gradient Compression (LQGCM), a variance-driven block stratified that transmits 88-bit meta tuples only, enabling codebook resonance replay, invertible vector quantization and Laplace-private gradients without any numeric payload or sparsity mask; (3) Energy-Minimisation Aggregation Model (EMAM), a closed-form update that mixes staleness weights, proxy-gradient correction and EMA momentum for stable convergence on lossy links. Experiments on five IIoT benchmarks show that EAFL-ALP increases accuracy by up to 32.1%, accelerates convergence 3.3×, lowers privacy leakage by 34.1%, and reduces communication volume by two orders of magnitude with no loss of model fidelity.

User trajectories are denser and highly dynamic in mobile crowdsensing (MCS) system, rendering traditional privacy budget allocation schemes insufficient. Additionally, the protection of semantic location privacy is often neglected in these schemes, making them vulnerable to inference attacks. To address these deficiencies, a user trajectory privacy protection strategy based on deep reinforcement learning is proposed in this article. First, a differential privacy-based user trajectory privacy protection algorithm (DP-upps) is designed to protect the privacy by perturbing the extracted trajectory feature points. Then, a deep reinforcement learning-based privacy budget allocation algorithm (DRL-pbas) is introduced. The privacy budget is dynamically adjusted by deep reinforcement learning option to continuously adapt to environmental changes and maximize benefits. After that, a DRL-pbas based user privacy protection strategy (DRL-UPPS) is proposed, integrating semantic location privacy protection. This approach combines the previous two algorithms, allowing the privacy budget to be allocated in a way that effectively balances the protection of physical and semantic location privacy and data quality. Ultimately, a large number of simulation experiments are conducted based on real datasets. The experiments demonstrate that DRL-UPPS can effectively balance privacy protection and data quality, resisting the privacy attacks. Compared with other strategies, DRL-UPPS improves comprehensive privacy protection capability by approximately 10% and data utility by approximately 8%.

Communication and sensing integration (CSI) technology underpins efficient data acquisition, real-time sensing, and intelligent decision-making in intelligent transportation systems (ITS). By merging communication and sensing, CSI enables seamless data sharing and collaborative learning within the internet of vehicles (IoV), while tackling the complexities of dynamic, heterogeneous environments. However, IoV systems still confront suboptimal resource allocation, synchronization bottlenecks in federated learning (FL), and the delicate balance between privacy and data utility, limiting scalability and deployment. To address these issues, this article presents a CSI-driven federated learning framework comprising three key modules: the polar-driven resource allocation mechanism (PDRAM), the polar-driven asynchronous federated update mechanism (AFUM), and the deep context-aware dynamic privacy budget allocation model (DC-DPBA). Leveraging polar coding principles, PDRAM optimizes communication channel allocation by prioritizing high-fidelity data on high-speed channels. AFUM adopts a vehicle performance score and a progressive aggregation strategy for asynchronous updates, mitigating synchronization challenges. Meanwhile, DC-DPBA uses deep learning and contextual information to dynamically adjust privacy budgets, striking a balance between data utility and privacy protection. Experimental results show that this framework increases transmission efficiency, model training accuracy, and privacy preservation by 22%, 17%, and 17%, respectively, compared to state-of-the-art approaches, offering a scalable and secure solution for CSI-driven IoV environments.

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.

Users can obtain intelligent services by sharing information in social networks. Big data technologies can discover underlying benefits from this information. However, stringent security concern is raised at the same time. The public data can be utilized by adversaries, which will bring dire consequences. In this paper, the influence maximization problem is investigated in a privacy protection environment, which aims to find a subset of secure users that can make the spread of influence maximization and privacy disclosure minimization. At first, in order to estimate the risk level for each user, a Bayesian-based individual privacy risk evaluation model is proposed to rank the individual risk levels. Secondly, as the aim is to measure the influence capability for each user, a cascade influence capability evaluation model is designed to rank the friend influence capability levels. Finally, based on these two factors, a privacy protection method is designed for solving the influence maximization with attack constraint problem. In addition, the comparison experiments show that our method can achieve the goal of influence maximization and privacy disclosure minimization efficiently.