A Trajectory Privacy Protection Scheme Based on Local Community Division

With the development of communication technology and widespread use of mobile devices, location-based services (LBS) have been fully developed, providing convenience for daily life. The accompanying issue of privacy leakage has received much attention. Most existing methods focus on perturbing spatial locations without considering the semantic attributes of location and other factors. Furthermore, these strategies are insufficient to defend against semantic inference attacks. Therefore, in this paper, we propose a trajectory reconstruction scheme based on local community division. It aims to anonymize sensitive locations contain privacy information, significantly reducing computational and storage costs. Experimental results show that this approach protects trajectory privacy and preserves data utility efficiently and effectively.