Privacy-Preserving Method for Trajectory Data Publication Based on Local Preferential Anonymity

Abstract

With the rapid development of mobile positioning technologies, location-based services (LBSs) have become more widely used. The amount of user location information collected and applied has increased, and if these datasets are directly released, attackers may infer other unknown locations through partial background knowledge in their possession. To solve this problem, a privacy-preserving method for trajectory data publication based on local preferential anonymity (LPA) is proposed. First, the method considers suppression, splitting, and dummy trajectory adding as candidate techniques. Second, a local preferential (LP) function based on the analysis of location loss and anonymity gain is designed to effectively select an anonymity technique for each anonymous operation. Theoretical analysis and experimental results show that the proposed method can effectively protect the privacy of trajectory data and improve the utility of anonymous datasets.