Privacy-preserving spatiotemporal trajectory generalization publishing scheme with differential privacy

Abstract

With the development of IoT and mobile sensing devices, trajectory data has an increasingly high research value. However, unauthorized data mining and analyzing will result in privacy violations. Therefore, the key issue is how to maintain user privacy while publishing usable data. To address the above problem, we propose a spatiotemporal trajectory data generalization publishing scheme with differential privacy (STG-DP), which consists of two components: trajectory processing and trajectory publishing. In trajectory processing, to improve data utility, a density-based trajectory clustering framework (DTC) is proposed, integrating two clustering algorithms to compare the impact of synthetic and real cluster centers on experimental results. In terms of trajectory publishing, an adaptive noise perturbation mechanism based on the staircase mechanism is proposed to enhance the degree of privacy protection. We theoretically prove that STG-DP satisfies the definition of differential privacy and experimentally verify it on a real dataset. The experiments demonstrate that STG-DP provides greater data utility and privacy protection than existing studies.