Synthesizing Realistic Trajectory Data With Differential Privacy

Abstract

Vehicle trajectory data is critical for traffic management and location-based services. However, the released trajectories raise serious privacy concerns because they contain sensitive information such as homes and workplaces. Based on differential privacy, this problem can be addressed by generating synthetic trajectories from the original sensitive data while guaranteeing personal privacy. Unfortunately, existing methods focus on synthesizing trajectory datasets that preserve summary-level statistics (e.g., the overall distribution of user movements), making these synthetic trajectories lose individual-level mobility patterns. As shown in our experiment, this results in the low performance of their synthetic datasets in real-world applications. To address these limitations, we propose a novel solution for Synthesizing Private and Realistic Trajectories, namely SPRT, whose key idea is to integrate the public geography structures of the target area into the process of private trajectory synthesis. This enables us to capture more accurate mobility patterns to synthesize realistic trajectories, which can preserve both summary-level statistics and individual-level mobility behaviors. Consequently, the synthetic trajectories generated by SPRT are more similar to real trajectories and therefore more practical. We evaluate the performance of SPRT in real-world applications by applying its synthetic data to a series of trajectory analytic tasks. The results demonstrate that our solution improves data utility by at least 37% over state-of-the-art approaches.