Department of Computer Science
Private proximity detection and monitoring with vicinity regions
Proximity detection is an important location-based service (LBS) that helps mobile users find nearby friends. However, this service usually requires users to submit their locations to the server, which raises privacy concerns. In this paper, we propose symmetric proximity detection on vicinity regions as a fair and privacy-preserving solution to this problem. Specifically, each user can specify a nearby area as vicinity region, and two users are considered to be in proximity only if they are located in each other's vicinity region. To enable efficient detection, we design a shift-and-compare protocol for one-shot proximity queries without revealing users' locations or vicinity regions. Furthermore, for continuous proximity monitoring, we propose an alert-area-based location update strategy with minimal update frequency, thereby saving communication cost. Experimental results demonstrate that our proposed privacy-preserving techniques achieve high efficiency in terms of both computation and communication costs. Copyright © 2013 ACM.
LBS, Location privacy, Proximity queries
Source Publication Title
MobiDE '13 Proceedings of the 12th International ACM Workshop on Data Engineering for Wireless and Mobile Acess
New York, United States
Link to Publisher's Edition
Lin, Xin, Haibo Hu, Hong Ping Li, Jianliang Xu, and Byron Choi. "Private proximity detection and monitoring with vicinity regions." MobiDE '13 Proceedings of the 12th International ACM Workshop on Data Engineering for Wireless and Mobile Acess (2013): 5-12.