Suppression techniques for privacy-preserving trajectory data publishing

摘要

In this paper, we study the problem of protecting privacy in trajectory datasets from adversaries who can exploit their partial knowledge to infer unknown locations. To efficiently solve this problem, we propose a tree-based indexing structure to store all trajectory data and develop pruning strategies. We provide two algorithms to find a safe counterpart of the original trajectory dataset by using the pruning strategies. Finally, our experimental results demonstrate the efficiency of the proposed algorithms.