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Issue No.04 - Oct.-Dec. (2013 vol.12)
pp: 62-70
Hong Ping Li , Hong Kong Baptist University
Haibo Hu , Hong Kong Baptist University
Jianliang Xu , Hong Kong Baptist University
Mobile geosocial networking services could be the killer app for next-generation mobile computing. However, the privacy issue--in particular, location privacy--has both users and government authors concerned. The authors address this issue for the "nearby friend alert" service, common in mobile geosocial networks. They review representative works on privacy-preserving proximity detection and present a new quantitative solution. They adopt the grid-and-hashing paradigm and develop optimal grid overlay and multilevel grids to increase the detection accuracy while saving the wireless bandwidth. Based on these techniques, they devise the client-side location update scheme and the server-side update handling procedure for continuous proximity detection. Simulation results show that their approach is efficient and scalable under various system parameters and user moving speeds.
Mobile communication, Mobile computing, Social network services, Privacy, Protocols, Mobile radio mobility management,pervasive computing, location privacy, geosocial network, location update, mobile computing
Hong Ping Li, Haibo Hu, Jianliang Xu, "Nearby Friend Alert: Location Anonymity in Mobile Geosocial Networks", IEEE Pervasive Computing, vol.12, no. 4, pp. 62-70, Oct.-Dec. 2013, doi:10.1109/MPRV.2012.82
1. A. Beresford and F. Stajano, “Location Privacy in Pervasive Computing,” IEEE Pervasive Computing, vol. 2, no. 1, 2003, pp. 46-55.
2. G. Myles, A. Friday, and N. Davies, “Preserving Privacy in Environments with Location-Based Applications,” IEEE Pervasive Computing, vol. 2, no. 1, 2003, pp. 56-64.
3. B. Gedik and L. Liu, “Location Privacy in Mobile Systems: A Personalized Anonymization Model,” Proc. 25th IEEE Int’l Conf. Distributed Computing Systems (ICDCS 05), IEEE, 2005, pp. 620-629.
4. G. Ghinita, P. Kalnis, and S. Skiadopoulos, “Prive: Anonymous Location-Based Queries in Distributed Mobile Systems,” Proc. 16th Int’l Conf. World Wide Web (WWW 07), ACM, 2007, pp. 371-380.
5. A. Khoshgozaran and C. Shahabi, “Blind Evaluation of Nearest Neighbor Queries Using Space Transformation to Preserve Location Privacy,” Advances in Spatial and Temporal Databases, LNCS 4605, Springer.
6. X. Pan, J. Xu, and X. Meng, “Protecting Location Privacy Against Location-Dependent Attack in Mobile Services,” Proc. 17th ACM Conf. Information and Knowledge Management, ACM, 2008, pp. 1475-1476.
7. T. Xu and Y. Cai, “Location Anonymity in Continuous Location-Based Services,” Proc. ACM Int’l. Symp. Geographic Information Systems (GIS 07), ACM, 2007, pp. 300-307.
8. H. Hu and J. Xu, “Non-Exposure Location Anonymity,” Proc. IEEE 25th Int’l Conf. Data Eng. (ICDE 09), IEEE, 2009, pp. 1120-1131.
9. C.-Y. Chow, M.F. Mokbel, and W.G. Aref, “Casper*: Query Processing for Location Services without Compromising Privacy,” ACM Trans. Database Systems, vol. 34, no. 4, 2009, article 24.
10. L. Šikšnys et al., “A Location Privacy Aware Friend Locator,” Advances in Spatial and Temporal Databases, LNCS 5644, Springer, 2009, pp. 405-410.
11. S. Mascetti et al., “Privacy in Geo-Social Networks: Proximity Notification with Untrusted Service Providers and Curious Buddies,” VLDB J., vol. 20, no. 4, 2011, pp. 541-566.
12. L. Šikšnys et al., “Private and Flexible Proximity Detection in Mobile Social Networks,” Proc. 11th Int’l Conf. Mobile Data Management (MDM 10), IEEE CS, 2010, pp. 75-84.
13. T. Brinkhoff and O. Str, “A Framework for Generating Network-Based Moving Objects,” Geoinformatica, vol. 6, no. 2, 2002, pp. 153-180.
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