A Road-Network Based Privacy-Preserving Approach in Trajectory Publishing

Abstract

The increasing abundance of data in the trajectories of personal movement opens up new opportunities for analyzing and mining human mobility. But the inherent personal information in the data raises the privacy concern. In this paper, we propose a novel approach to protect the privacy of trajectory data in publishing; it is the first paper to present the idea that publishing the trajectory data according to the road network. First, we partition the trajectory into several fragments, location points in each fragment lie on the same road segment. Second, we cluster trajectory fragments into subclasses according to their similar movement characteristics on the same road segment. Finally, we anonymize trajectories in each class by constructing reasonably representative trajectory. The performance of our approach is evaluated based on simulation and real-world trajectories datasets. And the results show the effectiveness of our approach.