Personalized Trajectory Privacy-preserving Scheme for Mobile Social Networks

Abstract

Mobile social networks are growing rapidly with the development of mobile terminals and wireless networks. However, the risk of leakage of trajectory privacy is increasing day by day. This paper proposed a personalized trajectory privacy preserving algorithm on mobile social networks. The algorithm solves the problem of over-protection under the traditional privacy protection according to the difference of privacy preserving requirements of mobile users. At the same time, the user forms an equivalence class by selecting a certain number of surrounding user partners to solve the privacy leakage problem caused by the excessive difference between the randomly generated false trajectory and the real trajectory. Finally, the experimental results show that the proposed scheme can generate trajectories similar to users, and the user's position and trajectory privacy average level exceeded 0.5, which proves the effectiveness of the algorithm.