BlackHole-DTP: a distributed trajectory privacy protection strategy with deep trajectory training and data blackhole

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%.