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Privacy Preserving in Location Data Release: A Differential Privacy Approach

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Part of the Lecture Notes in Computer Science book series (LNAI,volume 8862)

Abstract

Communication devices with GPS chips allow people to generate large volumes of location data. However, location datasets have been confronted with serious privacy concerns. Recently, several privacy techniques have been proposed but most of them lack a strict privacy notion, and can hardly resist the number of possible attacks. This paper proposes a private release algorithm to randomize location datasets in a strict privacy notion, differential privacy. This algorithm includes three privacy-preserving operations: Private Location Clustering shrinks the randomized domain and Cluster Weight Perturbation hides the weights of locations, while Private Location Selection hides the exact locations of a user. Theoretical analysis on utility confirms an improved trade-off between the privacy and utility of released location data. The experimental results further suggest this private release algorithm can successfully retain the utility of the datasets while preserving users’ privacy.

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  • DOI: 10.1007/978-3-319-13560-1_15
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References

  1. Abul, O., Bonchi, F., Nanni, M.: Anonymization of moving objects databases by clustering and perturbation. Information Systems 35(8), 884–910 (2010)

    CrossRef  Google Scholar 

  2. Blum, A., Dwork, C., McSherry, F., Nissim, K.: Practical privacy: The sulq framework, pp. 128–138 (2005), cited By (since 1996) 59

    Google Scholar 

  3. Blum, A., Ligett, K., Roth, A.: A learning theory approach to non-interactive database privacy. In: Proceedings of the 40th Annual ACM Symposium on Theory of Computing (2008)

    Google Scholar 

  4. Chatzikokolakis, K., Palamidessi, C., Stronati, M.: A predictive differentially-private mechanism for mobility traces. In: De Cristofaro, E., Murdoch, S.J. (eds.) PETS 2014. LNCS, vol. 8555, pp. 21–41. Springer, Heidelberg (2014)

    CrossRef  Google Scholar 

  5. Chen, R., Acs, G., Castelluccia, C.: Differentially private sequential data publication via variable-length n-grams. In: Proceedings of the 2012 ACM Conference on Computer and Communications Security, CCS 2012, pp. 638–649. ACM, New York (2012)

    CrossRef  Google Scholar 

  6. Dewri, R.: Local differential perturbations: Location privacy under approximate knowledge attackers. IEEE Transactions on Mobile Computing 12(12), 2360–2372 (2013)

    CrossRef  Google Scholar 

  7. 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)

    CrossRef  Google Scholar 

  8. Ho, S.-S., Ruan, S.: Differential privacy for location pattern mining. In: Proceedings of the 4th ACM SIGSPATIAL International Workshop on Security and Privacy in GIS and LBS, SPRINGL 2011, pp. 17–24. ACM, New York (2011)

    Google Scholar 

  9. Kido, H., Yanagisawa, Y., Satoh, T.: Protection of location privacy using dummies for location-based services. In: Proceedings of the 21st International Conference on Data Engineering Workshops, ICDEW 2005, p. 1248. IEEE Computer Society, Washington, DC (2005)

    Google Scholar 

  10. McSherry, F., Talwar, K.: Mechanism design via differential privacy. In: Proceedings of the 48th Annual IEEE Symposium on Foundations of Computer Science, FOCS 2007, pp. 94–103 (2007)

    Google Scholar 

  11. 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, VLDB 2006, pp. 763–774. VLDB Endowment (2006)

    Google Scholar 

  12. Nergiz, M.E., Atzori, M., Saygin, Y., Güç, B.: Towards trajectory anonymization: a generalization-based approach. Transactions on Data Privacy 2(1), 47–75 (2009)

    MathSciNet  Google Scholar 

  13. Pan, X., Xu, J., Meng, X.: Protecting location privacy against location-dependent attacks in mobile services. IEEE Transactions on Knowledge and Data Engineering 24(8), 1506–1519 (2012)

    CrossRef  Google Scholar 

  14. 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 (2011)

    Google Scholar 

  15. Zhu, T., Li, G., Zhou, W., Xiong, P., Yuan, C.: Deferentially private tagging recommendation based on topic model. In: Tseng, V.S., Ho, T.B., Zhou, Z.-H., Chen, A.L.P., Kao, H.-Y. (eds.) PAKDD 2014, Part I. LNCS, vol. 8443, pp. 557–568. Springer, Heidelberg (2014)

    CrossRef  Google Scholar 

  16. Zhu, T., Ren, Y., Zhou, W., Rong, J., Xiong, P.: An effective privacy preserving algorithm for neighborhood-based collaborative filtering. Future Generation Computer Systems 36, 142–155 (2014)

    CrossRef  Google Scholar 

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Xiong, P., Zhu, T., Pan, L., Niu, W., Li, G. (2014). Privacy Preserving in Location Data Release: A Differential Privacy Approach. In: Pham, DN., Park, SB. (eds) PRICAI 2014: Trends in Artificial Intelligence. PRICAI 2014. Lecture Notes in Computer Science(), vol 8862. Springer, Cham. https://doi.org/10.1007/978-3-319-13560-1_15

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  • DOI: https://doi.org/10.1007/978-3-319-13560-1_15

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-13559-5

  • Online ISBN: 978-3-319-13560-1

  • eBook Packages: Computer ScienceComputer Science (R0)