A Privacy-Preserving Trajectory Publication Method Based on Secure Start-Points and End-Points

Abstract

By judging whether the start-point and end-point of a trajectory conform to the user's behavioral habits, an attacker who possesses background knowledge can breach the anonymous trajectory. Traditional trajectory privacy preservation schemes often generate an anonymous set of trajectories without considering the security of the trajectory start- A nd end-points. To address this problem, this paper proposes a privacy-preserving trajectory publication method based on generating secure start- A nd end-points. First, a candidate set containing a secure start-point and end-point is generated according to the user's habits. Second, k-1 anonymous trajectories are generated bidirectionally according to that secure candidate set. Finally, accessibility corrections are made for each anonymous trajectory. This method integrates features such as local geographic reachability and trajectory similarity when generating an anonymized set of trajectories. This provides users with privacy preservation at the k-anonymity level, without relying on the trusted third parties and with low algorithm complexity. Compared with existing methods such as trajectory rotation and unidirectional generation, theoretical analysis and experimental results on the datasets of real trajectories show that the anonymous trajectories generated by the proposed method can ensure the security of trajectory privacy while maintaining a higher trajectory similarity.