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A Location Privacy Estimator Based on Spatio-Temporal Location Uncertainties

  • Arielle Moro
  • Benoît Garbinato
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10299)

Abstract

The proliferation of mobile devices and location-based services (LBS) is strongly challenging user privacy. Users disclose a large volume of sensitive information about themselves to LBS. Indeed, such services collect user locations to operate and can thus use them to perform various inference attacks. Several privacy mechanisms and metrics have been proposed in the literature to preserve location privacy and to quantify the level of privacy obtained when these mechanisms are applied on raw locations. Although the use of these metrics is relevant under specific threat models, they cannot anticipate the level of location privacy on the sole basis of the altered location data shared with LBS. Therefore, we propose a location privacy estimator that approximates the level of location privacy based on spatio-temporal uncertainties resulting from location alterations produced when a location privacy preserving mechanism is applied on user raw locations. This estimator also takes into account spatial-temporal user privacy parameters. We also describe the computation of the spatio-temporal uncertainties through the sampling, the Gaussian perturbation as well as the spatial cloaking. Finally, we compare the results of our estimator with those of the success of two localization attacks. The findings show that our estimator provides reasonable or conservative estimates of the location privacy level.

Keywords

Global Position System Location Privacy Inference Attack Temporal Uncertainty Location Uncertainty 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgment

This research is partially funded by the Swiss National Science Foundation in the context of Project 146714.

References

  1. 1.
    Ardagna, C.A., Cremonini, M., Damiani, E., Capitani di Vimercati, S., Samarati, P.: Location privacy protection through obfuscation-based techniques. In: Barker, S., Ahn, G.-J. (eds.) DBSec 2007. LNCS, vol. 4602, pp. 47–60. Springer, Heidelberg (2007). doi: 10.1007/978-3-540-73538-0_4 CrossRefGoogle Scholar
  2. 2.
    Armstrong, M.P., Rushton, G., Zimmerman, D.L.: Geographically masking health data to preserve confidentiality. Stat. Med. 18, 497–525 (1999)CrossRefGoogle Scholar
  3. 3.
    Barnes, S.B.: A privacy paradox: social networking in the united states. First Monday 11(9) (2006)Google Scholar
  4. 4.
    Beresford, A.R., Stajano, F.: Mix zones: user privacy in location-aware services. In: Proceedings of the Second IEEE Annual Conference on Pervasive Computing and Communications Workshops, 2004, pp. 127–131, March 2004Google Scholar
  5. 5.
    Cheng, R., Zhang, Y., Bertino, E., Prabhakar, S.: Preserving user location privacy in mobile data management infrastructures. In: Danezis, G., Golle, P. (eds.) PET 2006. LNCS, vol. 4258, pp. 393–412. Springer, Heidelberg (2006). doi: 10.1007/11957454_23 CrossRefGoogle Scholar
  6. 6.
    Damiani, M.L.: Location privacy models in mobile applications: conceptual view and research directions. GeoInformatica 18(4), 819–842 (2014)CrossRefGoogle Scholar
  7. 7.
    Duckham, M., Kulik, L.: Location privacy and location-aware computing. In: Dynamic & Mobile GIS: Investigating Change in Space and Time, pp. 34–51 (2006)Google Scholar
  8. 8.
    Gambs, S., Killijian, M.-O., del Prado, N., Cortez, M.: Show me how you move and i will tell you who you are. Trans. Data Priv. 4(2), 103–126 (2011)MathSciNetGoogle Scholar
  9. 9.
    Hoh, B., Gruteser, M.: Protecting location privacy through path confusion. In: SecureComm, pp. 194–205. IEEE (2005)Google Scholar
  10. 10.
    Hoh, B., Gruteser, M., Xiong, H., Alrabady, A.: Enhancing security and privacy in traffic-monitoring systems. IEEE Pervasive Comput. 5(4), 38–46 (2006)CrossRefGoogle Scholar
  11. 11.
    Hoh, B., Gruteser, M., Xiong, H., Alrabady, A.: Preserving privacy in gps traces via uncertainty-aware path cloaking. In: Proceedings of the 14th ACM Conference on Computer and Communications Security, CCS 2007, pp. 161–171. ACM, New York (2007)Google Scholar
  12. 12.
    Kehr, F., Kowatsch, T., Wentzel, D., Fleisch, E.: Thinking styles and privacy decisions: need for cognition, faith into intuition, and the privacy calculus. In: Smart Enterprise Engineering: 12. Internationale Tagung Wirtschaftsinformatik, WI 2015, Osnabrück, Germany, March 4–6, 2015, pp. 1071–1084 (2015)Google Scholar
  13. 13.
    Krumm, J.: Inference attacks on location tracks. In: LaMarca, A., Langheinrich, M., Truong, K.N. (eds.) Pervasive 2007. LNCS, vol. 4480, pp. 127–143. Springer, Heidelberg (2007). doi: 10.1007/978-3-540-72037-9_8 CrossRefGoogle Scholar
  14. 14.
    Krumm, J.: A survey of computational location privacy. Pers. Ubiquit. Comput. 13(6), 391–399 (2009)CrossRefGoogle Scholar
  15. 15.
    Kulkarni, V., Moro, A., Garbinato, B.: A mobility prediction system leveraging realtime location data streams: poster. In: Proceedings of the 22nd Annual International Conference on Mobile Computing and Networking, MobiCom 2016, pp. 430–432. ACM, New York (2016)Google Scholar
  16. 16.
    Laurila, J.K., Gatica-Perez, D., Aad, I., Blom, J., Bornet, O., Do, T.-M.-T., Dousse, O., Eberle, J., Miettinen, M.: The mobile data challenge: big data for mobile computing research. In: Pervasive Computing (2012)Google Scholar
  17. 17.
    Rebollo-Monedero, D., Parra-Arnau, J., Díaz, C., Forné, J.: On the measurement of privacy as an attacker’s estimation error. Int. J. Inf. Sec. 12(2), 129–149 (2013)CrossRefGoogle Scholar
  18. 18.
    Shokri, R., Freudiger, J., Hubaux, J.P.: A unified framework for location privacy. In: Proceedings of the 9th International Symposium on Privacy Enhancing Technologies (PETS 2010), pp. 203–214. Citeseer (2010)Google Scholar
  19. 19.
    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, pp. 21–30. ACM (2009)Google Scholar
  20. 20.
    Shokri, R., Theodorakopoulos, G., Le Boudec, J.-Y., Hubaux, J.-P.: Quantifying location privacy. In: Proceedings of the 2011 IEEE Symposium on Security and Privacy, SP 2011, pp. 247–262. IEEE Computer Society, Washington, DC (2011)Google Scholar
  21. 21.
    Xu, H., Wang, H., Stavrou, A.: Privacy risk assessment on online photos. In: Bos, H., Monrose, F., Blanc, G. (eds.) RAID 2015. LNCS, vol. 9404, pp. 427–447. Springer, Cham (2015). doi: 10.1007/978-3-319-26362-5_20 CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Université de LausanneLausanneSwitzerland

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