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Semantics-aware Obfuscation for Location Privacy

  • 발행 : 2008.06.30

초록

The increasing availability of personal location data pushed by the widespread use of location-sensing technologies raises concerns with respect to the safeguard of location privacy. To address such concerns location privacy-preserving techniques are being investigated. An important area of application for such techniques is represented by Location Based Services (LBS). Many privacy-preserving techniques designed for LBS are based on the idea of forwarding to the LBS provider obfuscated locations, namely position information at low spatial resolution, in place of actual users' positions. Obfuscation techniques are generally based on the use of geometric methods. In this paper, we argue that such methods can lead to the disclosure of sensitive location information and thus to privacy leaks. We thus propose a novel method which takes into account the semantic context in which users are located. The original contribution of the paper is the introduction of a comprehensive framework consisting of a semantic-aware obfuscation model, a novel algorithm for the generation of obfuscated spaces for which we report results from an experimental evaluation and reference architecture.

키워드

참고문헌

  1. ATALLAH, M. AND FRIKKEN, K. 2004. Privacy-preserving location-dependent query processing. In ACS/IEEE Intl. Conf. on Pervasive Services (ICPS), pages 9-17. IEEE Computer Society.
  2. BERESFORD, A. R. AND STAJANO, F. 2003. Location privacy in pervasive computing. IEEE Pervasive Computing, 2(1):46-55. https://doi.org/10.1109/MPRV.2003.1186725
  3. BETTINI, C., MASCETTI, S., WANG, X. S., AND JAJODIA, S. 2007. Anonymity in location-based services: Towards a general framework. In 2007 International Conference on Mobile Data Management, pages 69-76. IEEE.
  4. BONCHI, F., PEDRESCHI, D., TURINI, F., MALIN, B., VERYKIOS, V. S., MOELANS, B., AND SAYGIN, Y. 2007. Privacy protection: Regulations and technologies, opportunities and threats. In Mobility, Data Mining, and Privacy, pages 101-122. Springer.
  5. BRUN, L. AND KROPATSCH, W. 2006. Contains and inside relationships within combinatorial pyramids. Pattern Recognition, 39(4):515-526. https://doi.org/10.1016/j.patcog.2005.10.015
  6. CHENG, R., ZHANG, Y., BERTINO, E., AND PRABHAKAR, S. 2006. Preserving user location privacy in mobile data management infrastructures. In Proceedings of the 6th Workshop on Privacy Enhancing Technologies (PET'06), volume 4258 of Lecture Notes in Computer Science LNCS, pages 393-412. Springer Berlin/Heidelberg.
  7. DU, W. AND ATALLAH, M. J. 2001. Secure multi-party computation problems and their applications: a review and open problems. In NSPW '01: Proceedings of the 2001 workshop on New security paradigms, pages 13-22. ACM.
  8. DUCKHAM, M. AND KULIK, L. 2005. A formal model of obfuscation and negotiation for location privacy. In Pervasive Computing, volume 3468 of Lecture Notes in Computer Science LNCS, pages 152-170. Springer Berlin/Heidelberg.
  9. GRUTESER, M. AND GRUNWALD, D. 2003. Anonymous usage of location-based services through spatial and temporal cloaking. In MobiSys '03: Proceedings of the 1st international conference on Mobile systems, applications and services, pages 31-42. ACM Press.
  10. KALNIS, P., GHINITA, G., MOURATIDIS, K., AND PAPADIAS, D. 2007. Preventing location-based identity inference in anonymous spatial queries. IEEE Transactions on Knowledge and Data Engineering, 19(12):1719-1733. https://doi.org/10.1109/TKDE.2007.190662
  11. MACHANAVAJJHALA, A., GEHRKE, J., KIFER, D., AND VENKITASUBRAMANIAM, M. 2006. ldiversity: Privacy beyond k-anonymity. In 22nd IEEE International Conference on Data Engineering. IEEE Computer Society.
  12. MOKBEL, M. F., CHOW, C.-Y., AND AREF, W. G. 2006. The new casper: query processing for location services without compromising privacy. In VLDB'2006: Proceedings of the 32nd international conference on Very large data bases, pages 763-774. VLDB Endowment.
  13. MOLENAAR, M. 1998. An Introduction to the Theory of Spatial Object Modelling for GIS. CRC Press.
  14. Open GIS Consortium. Open GIS simple features specification for SQL, 1999. Revision 1.1.
  15. SWEENEY, L. 2002. Achieving k-anonymity privacy protection using generalization and suppression. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 10(5):571-588. https://doi.org/10.1142/S021848850200165X
  16. VERYKIOS, V., DAMIANI, M. L., AND GKOULALAS-DIVANIS, A. 2007. Privacy and security in spatio-temporal data. In F. Giannotti and D. Pedreschi, editors, Mobility, Data Mining, and Privacy-Geographic Knowledge Discovery, pages 213-242. Springer.
  17. WORBOYS, M. F. AND CLEMENTINI, E. 2001. Integration of imperfect spatial information. Journal of Visual Languages and Computing, 12(1):61-80. https://doi.org/10.1006/jvlc.2000.0187
  18. XIAO, X. AND TAO, Y. 2006. Personalized privacy preservation. In SIGMOD '06: Proceedings of the 2006 ACM SIGMOD international conference on Management of data, pages 229-240. ACM Press.

피인용 문헌

  1. An OpenLS privacy-aware middleware supporting location-based applications vol.9, pp.4, 2013, https://doi.org/10.1108/IJPCC-09-2013-0024