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Dynamic Modeling of Location Privacy Protection Mechanisms

  • Sophie Cerf
  • Sonia Ben Mokhtar
  • Sara Bouchenak
  • Nicolas Marchand
  • Bogdan Robu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10853)

Abstract

Mobile applications tend to ask for users’ location in order to improve the service they provide. However, aside from increasing their service utility, they may also store these data, analyze them or share them with external parties. These privacy threats for users are a hot topic of research, leading to the development of so called Location Privacy Protection Mechanisms. LPPMs often are configurable algorithms that enable the tuning of the privacy protection they provide and thus the leveraging of the service utility. However, they usually do not provide ways to measure the achieved privacy in practice for all users of mobile devices, and even less clues on how a given configuration will impact privacy of the data given the specificities of everyone’s mobility. Moreover, as most Location Based Services require the user position in real time, these measures and predictions should be achieved in real time. In this paper we present a metric to evaluate privacy of obfuscated data based on users’ points of interest as well as a predictive model of the impact of a LPPM on these measure; both working in a real time fashion. The evaluation of the paper’s contributions is done using the state of the art LPPM Geo-I on synthetic mobility data generated to be representative of real-life users’ movements. Results highlight the relevance of the metric to capture privacy, the fitting of the model to experimental data, and the feasibility of the on-line mechanisms due to their low computing complexity.

Keywords

Location privacy Control of computing systems Modeling Location Based Services Points of interest 

References

  1. 1.
    Andrés, M.E., Bordenabe, N.E., Chatzikokolakis, K., Palamidessi, C.: Geo-indistinguishability: differential privacy for location-based systems. In: CCS, pp. 901–914 (2013)Google Scholar
  2. 2.
    Bilogrevic, I., Huguenin, K., Jadliwala, M., Lopez, F., Hubaux, J.-P., Ginzboorg, P., Niemi, V.: Inferring social ties in academic networks using short-range wireless communications. In: WPES, pp. 179–188 (2013)Google Scholar
  3. 3.
    Cerf, S., Primault, V., Boutet, A., Ben Mokhtar, S., Birke, R., Bouchenak, S., Chen, L.Y., Marchand, N., Robu, B.: Pulp: achieving privacy and utility trade-off in user mobility data. In: 2017 IEEE 36th Symposium on Reliable Distributed Systems (SRDS), pp. 164–173. IEEE (2017)Google Scholar
  4. 4.
    Dong, K., Gu, T., Tao, X., Lu, J.: Complete bipartite anonymity: confusing anonymous mobility traces for location privacy. In: 2012 IEEE 18th International Conference on Parallel and Distributed Systems (ICPADS), pp. 205–212. IEEE (2012)Google Scholar
  5. 5.
    Dwork, C.: Differential privacy. In: Bugliesi, M., Preneel, B., Sassone, V., Wegener, I. (eds.) ICALP 2006. LNCS, vol. 4052, pp. 1–12. Springer, Heidelberg (2006).  https://doi.org/10.1007/11787006_1CrossRefGoogle Scholar
  6. 6.
    Franceschi-Bicchierai, L.: Redditor cracks anonymous data trove to pinpoint muslim cab drivers, January 2015. http://mashable.com/2015/01/28/redditor-muslim-cab-drivers/
  7. 7.
    Gambs, S., Killijian, M.-O., del Prado Cortez, M.N.: De-anonymization attack on geolocated data. In: 2013 12th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, pp. 789–797 (2013)Google Scholar
  8. 8.
    Gambs, S., Killijian, M.-O., del Prado Cortez, M.N.: 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.
    Gambs, S., Killijian, M.-O., del Prado Cortez, M.N.: Next place prediction using mobility Markov chains. In: Proceedings of the First Workshop on Measurement, Privacy, and Mobility, p. 3. ACM (2012)Google Scholar
  10. 10.
    Gedik, B., Liu, L.: A customizable k-anonymity model for protecting location privacy. Technical report, Georgia Institute of Technology (2004)Google Scholar
  11. 11.
    Li, N., Li, T., Venkatasubramanian, S.: t-Closeness: privacy beyond k-anonymity and l-diversity. In: IEEE 23rd International Conference on Data Engineering, ICDE 2007, pp. 106–115. IEEE (2007)Google Scholar
  12. 12.
    Machanavajjhala, A., Gehrke, J., Kifer, D., Venkitasubramaniam, M.: l-diversity: privacy beyond k-anonymity. In: Proceedings of the 22nd International Conference on Data Engineering, ICDE 2006, p. 24. IEEE (2006)Google Scholar
  13. 13.
    Maouche, M., Ben Mokhtar, S., Bouchenak, S.: Ap-attack: a novel user re-identification attack on mobility datasets. In: MobiQuitous. ACM (2017)Google Scholar
  14. 14.
    Micinski, K., Phelps, P., Foster, J.S.: An empirical study of location truncation on android. Weather 2, 21 (2013)Google Scholar
  15. 15.
    Mokbel, M.F., Chow, C.-Y., Aref, W.G.: The new Casper: query processing for location services without compromising privacy. In: Proceedings of the 32nd International Conference on Very Large Data Bases, pp. 763–774. VLDB Endowment (2006)Google Scholar
  16. 16.
    Pellegrini, S., Ess, A., Schindler, K., Van Gool, L.: You’ll never walk alone: modeling social behavior for multi-target tracking. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 261–268. IEEE (2009)Google Scholar
  17. 17.
    Primault, V., Ben Mokhtar, S., Lauradoux, C., Brunie, L.: Differentially private location privacy in practice. In: MoST 2014, San Jose, United States (2014)Google Scholar
  18. 18.
    Primault, V., Ben Mokhtar, S., Lauradoux, C., Brunie, L.: Time distortion anonymization for the publication of mobility data with high utility. In: TrustCom, pp. 539–546 (2015)Google Scholar
  19. 19.
    Sweeney, L.: k-anonymity: a model for protecting privacy. Int. J. Uncertain. Fuzziness Knowl.-Based Syst. 10(05), 557–570 (2002)MathSciNetCrossRefGoogle Scholar
  20. 20.
    Wernke, M., Skvortsov, P., Dürr, F., Rothermel, K.: A classification of location privacy attacks and approaches. Pers. Ubiquit. Comput. 18(1), 163–175 (2014)CrossRefGoogle Scholar
  21. 21.
    Wu, Y.-C., Sankararaman, K.A., Lafortune, S.: Ensuring privacy in location-based services: an approach based on opacity enforcement. IFAC Proc. Vol. 47(2), 33–38 (2014)CrossRefGoogle Scholar
  22. 22.
    Yavaş, G., Katsaros, D., Ulusoy, Ö., Manolopoulos, Y.: A data mining approach for location prediction in mobile environments. Data Knowl. Eng. 54(2), 121–146 (2005)CrossRefGoogle Scholar

Copyright information

© IFIP International Federation for Information Processing 2018

Authors and Affiliations

  • Sophie Cerf
    • 1
  • Sonia Ben Mokhtar
    • 2
  • Sara Bouchenak
    • 2
  • Nicolas Marchand
    • 1
  • Bogdan Robu
    • 1
  1. 1.Institute of Engineering, Univ. Grenoble Alpes, CNRS, Grenoble INP, GIPSA-labGrenobleFrance
  2. 2.INSA Lyon - CNRS - LIRIS, Distributed Systems Research GroupLyonFrance

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