DistPreserv: Maintaining user distribution for privacy-preserving Location-Based Services

• Main Authors: REN, Yanbing, LI, Xinghua, MIAO, Yinbin, DENG, Robert H., WENG, Jian, MA, Siqi, MA, Jianfeng
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2023
• Subjects: Incentive compatibility; Location distributions; Location privacy; Location-Based Services; Query accuracy; Information Security
• Online Access: https://ink.library.smu.edu.sg/sis_research/6930; https://ink.library.smu.edu.sg/context/sis_research/article/7933/viewcontent/DistPreserv_av.pdf
• Institution: Singapore Management University

Location-Based Services (LBSs) are one of the most frequently used mobile applications in the modern society. Geo-Indistinguishability (Geo-Ind) is a promising privacy protection model for LBSs since it can provide formal security guarantees for location privacy. However, Geo-Ind undermines the statistical location distribution of users on the LBS server because of perturbed locations, thereby disabling the server to provide distribution-based services (e.g., traffic congestion maps). To overcome this issue, we give a privacy definition, called DistPreserv, to enable the LBS server to acquire valid location distributions while providing users with strict location protection. Then we propose a privacy-preserving LBS scheme to benefit both users and the server, in which a location perturbation mechanism is designed to achieve the given definition under the guide of the incentive compatibility, and a retrieval area determination method is presented to ensure query accuracy of users by using the dynamic programming on the two-dimensional map plane. Finally, we theoretically prove that the designed mechanism can achieve the definition of DistPreserv and the property of incentive compatibility. Experimental explorations using a real-world dataset indicate that our proposal prominently improves the availability of users location distributions by over 90%, while providing high precision and recall of queries.