A Differential Privacy Based (k-ψ)-Anonymity Method for Trajectory Data Publishing

In recent years, mobile Internet technology and location based services have wide application. Application providers and users have accumulated huge amount of trajectory data. While publishing and analyzing user trajectory data have brought great convenience for people, the disclosure risks of user privacy caused by the trajectory data publishing are also becoming more and more prominent. Traditional k-anonymous trajectory data publishing technologies cannot effectively protect user privacy against attackers with strong background knowledge. For privacy preserving trajectory data publishing, we propose a differential privacy based (k-psi)-anonymity method to defend against re-identification and probabilistic inference attack. The proposed method is divided into two phases: in the first phase, a dummy-based (k-psi)-anonymous trajectory data publishing algorithm is given, which improves (k-delta)-anonymity by considering changes of threshold delta on different road segments and constructing an adaptive threshold set psi that takes into account road network information. In the second phase, Laplace noise regarding distance of anonymous locations under differential privacy is used for trajectory perturbation of the anonymous trajectory dataset outputted by the first phase. Experiments on real road network dataset are performed and the results show that the proposed method improves the trajectory indistinguishability and achieves good data utility in condition of preserving user privacy.