A Group-Correlated Privacy Protection Trajectory Publishing Method Based on Differential Privacy

Abstract

The group relationship (Community Relation) contained in the trajectory data can be used for hot spot exploration, community governance, and traffic diversion, which has broad application prospects. Trajectory group association privacy refers to the user relationship with a similar movement mode in the trajectory data. Publishing trajectory data to analysts without protection will cause the leakage of such privacy. Recently, trajectory correlation privacy has attracted the attention of researchers, proposing solutions based on differential privacy. Still, existing methods are limited to protecting the motion patterns of two users and cannot be used in multi-user scenarios. Moreover, existing methods use heuristic strategies to reconstruct trajectories, which have excessive noise increase and large loss of published trajectory availability. Because of the above problems, we design a probability differentiation tree (PDT) structure to describe the user's movement pattern, then define the probability differentiation tree similarity function. A noise probability differentiation tree generation algorithm (NPDT) is proposed to realize the trajectory of user-associated privacy protection by adding Laplace noise to the probability value of PDT. We also propose the trajectory reconstruction algorithm (TRA) to reconstruct each user trajectory through the noise probability differentiation tree, noise trajectory number distribution, and noise trajectory length distribution to form the final published trajectory data set. Theoretical analysis and experimental results show that the proposed privacy protection method effectively maintains the availability of trajectory data while improving the privacy protection intensity of group association.