EPCQ: Efficient Privacy-Preserving Contact Query Processing over Trajectory Data in Cloud

With the expansion of cloud computing, its effectiveness in handling large datasets, particularly trajectory, has become evident. However, due to the sensitivity of trajectory data, sharing it in plaintext could lead to privacy risks. It is a challenge to implement secure and efficient contact query based on trajectory data in the cloud. In this paper, we propose a privacy-preserving contact query processing over trajectory data. The trajectory vectorization is designed to encrypt trajectories that support secure distance comparison through vector inner product. Based on the trajectory vectors, the baseline privacy-preserving contact query processing scheme (EPCQ) is introduced. To improve the efficiency of contact query, the quick-filtering checkpoint optimization strategy is presented. By adopting it, the enhanced privacy-preserving contact query processing scheme (EPCQ+) is introduced. Experimental results show that the proposed schemes perform well in query accuracy and efficiency.