Personalized trajectory data perturbation algorithm based on quadtree indexing

To solve the privacy leakage problem of truck trajectories in intelligent logistics, this paper proposes a quadtree-based personalized joint location perturbation(QPJLP) algorithm using location generalization and local differential privacy(LDP) techniques. Firstly, a flexible position encoding mechanism based on the spatial quadtree indexing is designed, and the length of the encoding can be adjusted freely according to data availability. Secondly, to meet the privacy needs of different locations of users, location categories are introduced to classify locations as sensitive and ordinary locations. Finally, the truck invokes the corresponding mechanism in the QPJLP algorithm to locally perturb the code according to the location category, allowing the protection of non-sensitive locations to be reduced without weakening the protection of sensitive locations, thereby improving data availability. Simulation experiments demonstrate that the proposed algorithm effectively meets the personalized trajectory privacy requirements while also exhibiting good performance in trajectory proportion estimation and top-k classification.