Abstract

We propose a novel technique to ensure location privacy for mobility data using differential privacy. Privacy is guaranteed through path perturbation by injecting noise to both the space and time domain of a spatio-temporal data. In addition, we present to the best of our knowledge, the first context aware differential private algorithm. We conducted numerous experiments on real and synthetic datasets, and show that our approach produces superior privacy results when compared to state-of-the-art techniques.

Keywords

Spatial Database LBS Differential Privacy 

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References

  1. 1.
    Ashbrook, D., Starner, T.: Using GPS to learn significant locations and predict movement across multiple users. UbiComp (2003)Google Scholar
  2. 2.
    Osman, A., Francesco, B., Mirco, N.: Never walk alone: Uncertainty for anonymity in moving objects databases. In: ICDE (2008)Google Scholar
  3. 3.
    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)CrossRefGoogle Scholar
  4. 4.
    Friedman, A., Schuster, A.: Data mining with differential privacy. In: KDD (2010)Google Scholar
  5. 5.
    Kalnis, P., Ghinita, G., Mouratidis, K., Papadias, D.: Preventing Location-Based Identity Inference in Anonymous Spatial Queries. IEEE Trans. on Knowl. and Data Eng. (2007)Google Scholar
  6. 6.
    Mohamed, F.: Query processing for location services without compromising privacy. In: VLDB (2006)Google Scholar
  7. 7.
    Manolis, T., Nikos, M.: Privacy Preservation in the Publication of Trajectories. In: MDM (2008)Google Scholar
  8. 8.
    Mcsherry, F., Talwar, K.: Mechanism design via differential privacy. In: FOC (2007)Google Scholar
  9. 9.
    Dwork, C., McSherry, F., Nissim, K., Smith, A.: Calibrating noise to sensitivity in private data analysis. In: Halevi, S., Rabin, T. (eds.) TCC 2006. LNCS, vol. 3876, pp. 265–284. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  10. 10.
    Mcsherry, F.: Privacy integrated queries. In: SIGMOD (2009)Google Scholar
  11. 11.
    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)CrossRefGoogle Scholar
  12. 12.
    Ardagna, C.A., Cremonini, M., Damiani, E., De Capitani di Vimercati, S., Samarati, P.: Location Privacy Protection Through Obfuscation-Based Techniques. In: Barker, S., Ahn, G.-J. (eds.) Data and Applications Security 2007. LNCS, vol. 4602, pp. 47–60. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  13. 13.
    Hoh, B., Gruteser, M.: Protecting Location Privacy Through Path Confusion. In: SECURECOMM (2005)Google Scholar
  14. 14.
    Yu, Z., Li, Q., Chen, Y., Xie, X.: Understanding Mobility Based on GPS Data. In: UbiComp (2008)Google Scholar

Copyright information

© ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering 2013

Authors and Affiliations

  • Roland Assam
    • 1
  • Marwan Hassani
    • 1
  • Thomas Seidl
    • 1
  1. 1.RWTH Aachen UniversityGermany

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