The dummy-based trajectory privacy protection method to resist correlation attacks in Internet of Vehicles

Existing dummy-based trajectory privacy protection schemes do not take into account the correlation of multiple locations and whether the generated trajectory based on dummies matches the user's movement mode, which enables the adversary to identify some dummies. Aiming at this problem, to ensure that the generated trajectories match the movement modes of users, historical query trajectories of users are selected. In this way, the generated dummies on the selected historical query trajectories are based on the location relationship of adjacent time and the background information constraint of the dummy, namely, they should meet the time reachability, the similarity of historical query probability and the maximum in-degree. Security analysis shows that the proposed scheme effectively perturbs the spatiotemporal correlation between the real location and dummies. Furthermore, the proposed scheme is compared with the existing schemes in terms of single-point location exposure risk and trajectory exposure risk, and the experimental results indicate that the proposal has significant improvement in location privacy protection of the user.