A Privacy-Preserving and Research-Utilizable Trajectory Generator via Deep Generative Approach
Xinyue Sun, Tianyu Wo · 2023
Large-scale trajectory data is critical for a smart city to improve the efficiency of a transportation system. However, the release of original trajectory data violates privacy protection principles if no privacy-preserving approach is adopted, that’s why the public trajectory data for research is so limited. To enable extensive available trajectory data publishing, we propose a Privacy-Preserving and research-utilizable Trajectory Generator (PPTG), which uses a deep generative model to provide utilizable synthetic trajectories. Specifically, PPTG can not only extract the intrinsic and spatial features, but also get rid of possible privacy information from the real trajectories. In experiments, we show that the privacy-preserving trajectory data generated by PPTG can achieve superior performance in terms of privacy protection and data utility against the existing approaches.