PPTD: Preserving personalized privacy in trajectory data publishing by sensitive attribute generalization and trajectory local suppression
Trajectory data often provide useful information that can be used in real-life applications, such as traffic management, Geo-marketing, and location-based advertising. However, a trajectory database may contain detailed information about moving objects and associate them with sensitive attributes, such as disease, job, and income. Therefore, improper publishing of the trajectory database can put the privacy of moving objects at risk, especially when an adversary uses partial trajectory information as its background knowledge. The existing approaches for privacy preservation in trajectory data publishing provide the same privacy protection for all moving objects. The consequence is that some moving objects may be offered insufficient privacy protection, while some others may not require high privacy protection. In this paper, we address this problem and present PPTD, a novel approach for preserving privacy in trajectory data publishing based on the concept of personalized privacy. It aims to strike a balance between the conflicting goals of data utility and data privacy in accordance with the privacy requirements of moving objects. To the best of our knowledge, this is the first paper that combines sensitive attribute generalization and trajectory local suppression to achieve a tailored personalized privacy model for trajectory data publishing. Our experiments on two synthetic trajectory datasets suggest that PPTD is effective for preserving personalized privacy in trajectory data publishing. In particular, PPTD can significantly improve the data utility of anonymized trajectory databases when compared with previous work in the literature.
personalized privacy trajectory data generalization local suppression privacy preservation privacy attack information loss disclosure risk