TPPG: Privacy-preserving trajectory data publication based on 3D-Grid partition

摘要

The issue of privacy preservation is receiving more and more attention when publishing trajectory data. In this paper, we study the challenges of published trajectory data anonymization. Most existing anonymization methods directly delete the trajectories or locations violating specific constraints, it is likely to cause a large loss of information. To address the problem, this paper proposes a trajectory privacy preservation method based on 3D-Grid partition in order to reduce information loss in the process of trajectory anonymization. This method first divides the trajectory region into several spatio-temporal units (denoted as 3D-cells), and then conducts location exchange or suppression in each spatio-temporal unit. Based on the trajectory data partition, within each 3D-cell, the proposed method exchanges locations among trajectories or removes very few locations of some sub-trajectories which do not meet the conditions rather than the whole trajectory. Our method considers three scenarios of trajectory distribution and measures trajectory similarity based on time, orientation, spatial locations and other features of trajectory. After the reconstruction of the related anonymous sub-trajectories, an anonymized trajectory dataset is obtained. Theoretical analysis and experimental results show that, compared to other methods, the proposed algorithm effectively preserves trajectory data privacy and improves the anonymous results of trajectory data in terms of accuracy and availability.