A trajectory privacy protection method based on the replacement of points of interest in hotspot regions

Abstract

Location-Based Services (LBS) already provides technical support for advertising, bus scheduling, and personnel tracking. However, the trajectory data published in LBS contains some sensitive semantic information related users in some locations. Through mining these data, sensitive personal information can be disclosed, such as user’s living habits, interests, daily activities, social relations, and health condition. It is a challenge to provide users with high-quality LBS while protecting user privacy. In order to address the disadvantages of current trajectory privacy protection methods, we propose a method of trajectory privacy protection with the replacement of points of interest ($POIs$) based on hotspot clustering. Firstly, user stay points are extracted based on the speed threshold using a sliding time window, user stay areas are merged by the distance threshold based on user stay points, and user hotspot regions are extracted from all user stay areas using $DBSCAN$. Then, according to the semantic and distance features of the $POIs$ in the hotspot regions, the sensitive regions meeting the user’s privacy needs are constructed, and the $POIs$ are replaced in the sensitive regions according to the privacy budgets. Finally, some locations in the sensitive regions are reconstructed to minimize the trajectory change. The experimental results show that our method can improve the usability of protected trajectories about 13.8% to 16.5% compared to the differential privacy method under the same level of privacy protection.