RNC-DP: A personalized trajectory data publishing scheme combining road network constraints and GAN

Abstract

The popularity of location-based services facilitates people’s lives to a certain extent and generates a large amount of trajectory data. Analyzing these data can contribute to society’s development and provide better location services for users, but it also faces the security problem of personal trajectory privacy leakage. However, existing methods often suffer from either excessive privacy protection or insufficient protection of individual privacy. Therefore, this paper proposes a personalized trajectory data publishing scheme combining road network constraints and GAN (RNC-DP). Firstly, after grid-representing the trajectory data, we remove the unreachable grids and define a trajectory generation constraint. Second, the proposed TraGM model synthesizes the trajectory data to meet the constraints. Again, during the trajectory data publishing process, the proposed TraDP mechanism performs k-means clustering on the synthesized trajectories and assigns appropriate privacy budgets to the clustered generalized trajectory location points. Finally, the protected trajectory data is published. Compared with the existing schemes, the proposed scheme improves privacy protection strength by 10.2%–41.2% while balancing data availability and has low time complexity.