ABSTRACT
As devices move within a cellular network, they register their new location with cell base stations to allow for the correct forwarding of data. We show it is possible to identify a mobile user from these records and a pre-existing location profile, based on previous movement. Two different identification processes are studied, and their performances are evaluated on real cell location traces. The best of those allows for the identification of around 80% of users. We also study the misidentified users and characterise them using hierarchical clustering techniques. Our findings highlight the difficulty of anonymizing location data, and firmly establish they are personally identifiable.
- MIT Media Lab: Reality Mining. http://reality.media.mit.edu/, 2007.Google Scholar
- A. Beresford. Location Privacy in Ubiquitous Computing. PhD thesis, University of Cambridge, 2004.Google Scholar
- A. Bhattacharya and S. Das. LeZi-Update: An Information-Theoretic Framework for Personal Mobility Tracking in PCS Networks. Wireless Networks, 8(2):121--135, 2002. Google ScholarDigital Library
- D. Cvrcek, M. Kumpost, V. Matyas, and G. Danezis. A study on the value of location privacy. In A. Juels and M. Winslett, editors, WPES, pages 109--118. ACM, 2006. Google ScholarDigital Library
- C. Goemans and J. Dumortier. Enforcement issues - mandatory retention of traffic data in the eu: possible impact on privacy and on-line anonymity. Digital Anonymity and the Law, series IT & Law, pages 161--183, 2003.Google Scholar
- D. Gu and S. Rappaport. A dynamic location tracking strategy for mobile communicationsystems. Vehicular Technology Conference, 1998. VTC 98. 48th IEEE, 1, 1998.Google Scholar
- S. Salvador and P. Chan. Determining the number of clusters/segments in hierarchical clustering/segmentation algorithms. Tools with Artificial Intel ligence, 2004. ICTAI 2004. 16th IEEE International Conference on, pages 576--584, 2004. Google ScholarDigital Library
- B. Sidhu and H. Singh. Location Management in Cellular Networks. In Proceedings of World Academy of Science, Engineering and Technology, pages 314--319, 2007.Google Scholar
Index Terms
- Identification via location-profiling in GSM networks
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