UdpTrace: Utility-enhanced differential privacy scheme for trajectory data publishing

Abstract

Differential privacy is a popular method for preserving privacy in trajectory data publishing, allowing the utilization of user data while protecting sensitive information. However, trajectory data publishing under differential privacy often suffers from low utility and limits its value for meaningful analysis. In this paper, we propose UdpTrace, a utility-enhanced differential privacy scheme for trajectory data publishing. First, we introduce a method for discretizing geographic regions based on trajectory density. This method employs a coarse first-layer grid to enhance privacy protection and a fine second-layer grid to capture detailed trajectory information, thereby maintaining privacy while improving the application value of the data. Second, we build a semantic trip transfer matrix by aggregating the location label transfer probabilities and the cell transfers under the corresponding labels to maintain the semantic connection between the start and end regions. Third, we develop a novel sampling method to generate realistic intermediate trajectory points leveraging local transition patterns captured in the second-layer grid. Experimental results indicate that the proposed scheme effectively preserves privacy while significantly improving the utility of trajectory data.