Trajectory Privacy Protection Method Based on Sensitivity Analysis

With the widespread popularity of mobile devices, users continue to generate a variety of trajectory data in their daily activities. However, trajectory data often contains sensitive information of users, and direct disclosure of such data may lead to the leakage of user privacy. In order to solve the above problems, In this paper, we propose a Trajectory Privacy Protection Method Based on Sensitivity Analysis (TPPSA), combined with differential privacy-preserving strategies to improve data availability while ensuring privacy-preserving utility. In the data preprocessing stage, the data is segmented using temporal and spatial dimensions to effectively reduce noise injection. Semantic location and check-in frequency are introduced to construct a sensitivity analysis model for trajectory data, and the privacy budget is allocated according to the semantic relevance and sensitivity of historical trajectories to improve privacy protection effectiveness and data availability. By constructing a prefix tree, the data is hierarchically noised based on Markov probabilistic prediction model, and the noised trajectories and their counts are published. We verify the privacy and utility of this scheme by conducting experiments on real datasets.