With the widespread adoption of graph-structured data, protecting the complex relational information between nodes and edges while preventing sensitive information leakage has become a critical challenge. However, existing edge protection methods either introduce noise directly into the adjacency matrix, resulting in significant information loss, or uniformly apply noise across all edges, leading to imbalanced privacy budget allocation and inefficiency. To address these issues, we propose DPEI, a Differential Privacy-based Edge Information protection solution designed to safeguard the edge relationships between two nodes, thus reducing the risk of privacy leakage and preventing attackers from repeatedly inferring internal community relationships from the released graph data. Specifically, DPEI achieves protection through PPO (Proximal Policy Optimization)based selection of locally optimal thresholds combined with adaptive Laplace noise operations, and attachment nodes below the threshold into high-information edges to enhance relational information protection. Subsequently, unlike traditional uniform allocation, DPEI distributes the privacy budget in proportion to the information content of each edge, ensuring that edges with higher information content receive stronger privacy protection. Extensive experiments conducted on three real-world graph datasets demonstrate that DPEI significantly outperforms existing methods across seven commonly used graph metrics, thereby validating its effectiveness and practicality.
Spatial Mobile Crowdsourcing (SMC) faces the dual challenge of ensuring privacy while managing dynamic, Non-IID spatio-temporal data. While Federated Learning (FL) offers a privacy-preserving solution, traditional aggregation suffers from severe model drift due to evolving spatio-temporal contexts. Furthermore, existing approaches often decouple prediction from scheduling, failing to translate predictive insights into tangible task allocation efficiency. To address these challenges, we propose STDFL, a prediction-driven dynamic framework tailored for SMC. It features a hierarchical architecture combining client-side lightweight models for micro-patterns and a server-side Transformer for global dependencies. To mitigate drift, our Dynamic Spatio-Temporal Perceiving Aggregation adaptively weights updates based on spatial similarity and temporal freshness. For privacy, we integrate client-level <inline-formula><tex-math notation="LaTeX">$(\epsilon, \delta )$</tex-math><alternatives><mml:math><mml:mrow><mml:mo>(</mml:mo><mml:mi>ε</mml:mi><mml:mo>,</mml:mo><mml:mi>δ</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="wang-ieq1-3673177.gif"/></alternatives></inline-formula>-Differential Privacy to ensure formal protection. Finally, we introduce a Prediction-Driven Scheduler (PDS) that leverages predictive potentials for bipartite matching, theoretical analysis and stress tests confirm PDS achieves linear scalability and zero policy training cost, offering a superior real-time deployment trade-off compared to RL approaches. Experiments on Chengdu and Nanjing datasets show STDFL significantly outperforms SOTA baselines in efficiency and fairness,while achieving near-centralized prediction accuracy under rigorous privacy guarantees and effectively bridging the utility–privacy trade-off in spatial crowdsourcing.
Mingyue Li, Zhihao Wang, Caixia Ma et al.· IEEE Transactions on Mobile...· 0 citations