Privacy-Preserving Contact Query Processing Over Trajectory Data in Mobile Cloud Computing
Qu Lu, Hua Dai, Pengyue Li, Shuyan Wan, Geng Yang, Yang Xiang, Fu Xiao · IEEE Transactions on Mobile Computing · 2024
With the expansion of mobile devices and cloud computing, massive spatial trajectory data is generated and outsourced to the cloud for storage and analysis, enabling location-based mobile computing services. However, due to the sensitivity of the trajectory data, sharing it in plaintext could lead to privacy risks, especially in operations like contact queries. Thus, achieving secure and efficient contact queries based on the trajectory data in the cloud is a significant challenge. In this paper, we propose a privacy-preserving contact query processing over trajectory data in mobile cloud computing. The projection-based secure trajectory encoding is designed to convert trajectories into secure codes such that the comparison between the distance of two moving objects and the contact distance threshold is transformed into a problem of secure code matching. Adopting the secure code matching method, a baseline privacy-preserving contact query processing is proposed. To improve the query accuracy and efficiency, an amplification factor, an HTG-index and a filter table are designed for query processing optimization, based on which an enhanced privacy-preserving contact query processing is proposed. The game stimulation-based security analysis and experimental results show that the proposed query scheme is secure and performs well in query accuracy and efficiency.