Abstract
As location-based services emerge, many people feel exposed to high privacy threats. Privacy protection is a major challenge for such applications. A broadly used approach is perturbation, which adds an artificial noise to positions and returns an obfuscated measurement to the requester. Our main finding is that, unless the noise is chosen properly, these methods do not withstand attacks based on probabilistic analysis. In this paper, we define a strong adversary model that uses probability calculus to de-obfuscate the location measurements. Such a model has general applicability and can evaluate the resistance of a generic location-obfuscation technique. We then propose UniLO, an obfuscation operator which resists to such an adversary. We prove the resistance through formal analysis. We finally compare the resistance of UniLO with respect to other noise-based obfuscation operators.
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Ardagna, C.A., Cremonini, M., De Capitani di Vimercati, S., Samarati, P.: An obfuscation-based approach for protecting location privacy. IEEE Transactions on Dependable and Secure Computing 8(1), 13–27 (2011)
Ardagna, C.A., Cremonini, M., Gianini, G.: Landscape-aware location-privacy protection in location-based services. Journal of Systems Architecture 55(4), 243–254 (2009)
Barkuus, L., Dey, A.: Location-based services for mobile telephony: a study of users privacy concerns. In: Proceedings of the INTERACT 2003, 9th IFIP TC13 International Conference on Human-Computer Interaction, pp. 709–712 (July 2003)
Beresford, A.R., Stajano, F.: Location privacy in pervasive computing. IEEE Pervasive Computing 2(1), 46–55 (2003)
Conway, R., Strip, D.: Selective Partial Access to a Database. In: Proceedings of the 1976 Annual Conference, pp. 85–89. ACM (1976)
D’Roza, T., Bilchev, G.: An overview of location-based services. BT Technology Journal 21(1), 20–27 (2003)
Duckham, M., Kulik, L.: A Formal Model of Obfuscation and Negotiation for Location Privacy. In: Gellersen, H.-W., Want, R., Schmidt, A. (eds.) PERVASIVE 2005. LNCS, vol. 3468, pp. 152–170. Springer, Heidelberg (2005)
Duckham, M., Mason, K., Stell, J., Worboys, M.: A formal approach to imperfection in geographic information. Computer, Environment and Urban Systems 25, 89–103 (1999)
Duri, S., Gruteser, M., Liu, X., Moskowitz, P., Perez, R., Singh, M., Tang, J.M.: Framework for security and privacy in automotive telematics. In: Proceedings of the 2nd International Workshop on Mobile Commerce, pp. 25–32. ACM (2002)
Espinoza, F., Persson, P., Sandin, A., Nyström, H., Cacciatore, E., Bylund, M.: GeoNotes: Social and navigational aspects of location-based information systems. Tech. Rep. T2001/08, Swedish Institute of Computer Science (SICS) (May 2001)
Fortune, S.: A sweepline algorithm for voronoi diagrams. In: Proceedings of the Second Annual ACM SIGACT/SIGGRAPH Symposium on Computational Geometry, SCG 1986, pp. 313–322. ACM (1986)
Gruteser, M., Grunwald, D.: Anonymous Usage of Location-Based Services Through Spatial and Temporal Cloaking. In: Proceedings of the MobiSys 2003: 1st International Conference on Mobile Systems, Applications and Services, pp. 31–42 (2003)
Hofmann-Wellenhof, B., Lichtenegger, H., Collins, J.: Global Positioning System: Theory and Practice. Springer (2001)
Krumm, J.: A survey of computational location privacy. Personal and Ubiquitous Computing 13(6), 391–399 (2008)
Mascetti, S., Bettini, C., Freni, D., Wang, X.S., Jajodia, S.: Privacy-Aware Proximity Based Services. In: Proceedings of the MDM 2009: 10th International Conference on Mobile Data Management: Systems, Services and Middleware, pp. 31–40. IEEE (2009)
Myles, G., Friday, A., Davies, N.: Preserving privacy in environments with location-based applications. IEEE Pervasive Computing 2(1), 56–64 (2003)
Pal, A.: Localization algorithms in wireless sensor networks: Current approaches and future challenges. Network Protocols and Algorithms 2(1), 45–74 (2010)
Samarati, P., Sweeney, L.: Protecting privacy when disclosing information: k-anonymity and its enforcement through generalization and suppression. Tech. rep., Computer Science Laboratory SRI International (1998)
Schneier, B.: Secrecy, security, and obscurity (May 2002), http://www.schneier.com/crypto-gram-0205.html
Shokri, R., Freudiger, J., Jadliwala, M., Hubaux, J.P.: A distortion-based metric for location privacy. In: Proceedings of the 8th ACM Workshop on Privacy in the Electronic Society, WPES 2009, pp. 21–30. ACM (2009)
Zandbergen, P.A.: Accuracy of iPhone locations: A comparison of assisted GPS, WiFi and cellular positioning. Transactions in GIS 13(s1), 5–26 (2009)
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Dini, G., Perazzo, P. (2012). Uniform Obfuscation for Location Privacy. In: Cuppens-Boulahia, N., Cuppens, F., Garcia-Alfaro, J. (eds) Data and Applications Security and Privacy XXVI. DBSec 2012. Lecture Notes in Computer Science, vol 7371. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31540-4_7
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DOI: https://doi.org/10.1007/978-3-642-31540-4_7
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