Differentially Private Moving Object Database Publication in Location Tracking Service

Abstract

Location tracking applications which receives frequent updates of a moving object's position, collect numerous moving objects' location data. Public transit agencies can make use of tracking data to optimize traffic control strategies. While improper use of trajectory data could cause individuals' privacy leakage. However, existing privacy-preserving techniques are unable to provide sufficient privacy protection. In this paper, we propose a data-dependent differentially private sanitization algorithm to publish moving object database. Moreover, we make use of a set of real-world constraints to conduct constraint inference, which can boost the utility of the published data. At last, we experimentally evaluate the utility of the sanitized data in terms of range-count queries, results show high utility and efficiency of our proposal.