A Trajectory Privacy Protection Method Based on Random Sampling Differential Privacy

With the popularity of location-aware devices (e.g., smart phones), a large number trajectory data were collected. The dataset can be used in many fields including traffic monitoring, market analysis, city management, etc. collection and release will raise serious privacy concerns for users. If users’ is not protected enough, they refuse to share their data. In this paper, new protection method based on random sampling differential (TPRSDP), which provide more security protection, proposed. Compared with other methods, it takes less time run method. Experiments are conducted two real world datasets validate proposed scheme, results compared others terms running information loss. performance scheme different parameter values verified. setting parameters discussed detail, some valuable suggestions given.