Publication Type

Journal Article

Version

acceptedVersion

Publication Date

6-2023

Abstract

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.

Keywords

Incentive compatibility, Location distributions, Location privacy, Location-Based Services, Query accuracy

Discipline

Information Security

Research Areas

Cybersecurity

Publication

IEEE Transactions on Mobile Computing

Volume

22

Issue

6

First Page

3287

Last Page

3302

ISSN

1536-1233

Identifier

10.1109/TMC.2022.3141398

Publisher

Institute of Electrical and Electronics Engineers

Additional URL

https://doi.org/10.1109/TMC.2022.3141398

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