Differential Privacy Trajectory Data Publishing Based on Orientation Control

With the continuous expansion of differential privacy and its applications, its application in the privacy protection field of trajectory data release has received extensive attention. However, most existing research methods use the Kmeans to cluster the trajectory, , which cannot guarantee the final convergence due to the fact that the trajectory datasets under differential privacy constraints are usually disturbed by noise. To addrss this, this paper proposes an orientation control-based differential privacy-preserving trajectory data publishing method. Firstly, a trajectory generalization algorithm based on SKmeans|| clustering is proposed, which updates the centroid via a direction control mechanism during iterative process of clustering, and designs a scoring function in the index mechanism to control the convergence of the centroid, such that the quality of high dimensional data clustering can be improved. Secondly, a trajectory data publishing algorithm based on bounded noise mechanism is designed, which improves the availability of trajectory data after publishing. Meanwhile, the bounded noise mechanism ensures the true count of the hidden trajectory. Finally, the effectiveness of the method proposed in this paper is evaluated by experiments.