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Optimizing Noise Level for Perturbing Geo-location Data

  • Abhinav PaliaEmail author
  • Rajat Tandon
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 887)

Abstract

With the tremendous increase in the number of smart phones, App stores have been overwhelmed with applications requiring geo-location access in order to provide their users better services through personalization. Revealing a user’s location to these third party Apps, no matter at what frequency, is a severe privacy breach which can have unpleasant social consequences. In order to prevent inference attacks derived from geo-location data, a number of location obfuscation techniques have been proposed in the literature. However, none of them provides any objective measure of privacy guarantee. Some work has been done to define differential privacy for geo-location data in the form of geo-indistinguishability with l privacy guarantee. These techniques do not utilize any prior background information about the Points of Interest (PoIs) of a user and apply Laplacian noise to perturb all the location coordinates. Intuitively, the utility of such a mechanism can be improved if the noise distribution is derived after considering some prior information about PoIs. In this paper, we apply the standard definition of differential privacy on geo-location data. We use first principles to model various privacy and utility constraints, prior background information available about the PoIs (distribution of PoI locations in a 1D plane) and the granularity of the input required by different types of apps, in order to produce a more accurate and a utility maximizing differentially private algorithm for geo-location data at the OS level. We investigate this for a particular category of Apps and for some specific scenarios. This will also help us to verify whether Laplacian noise is still the optimal perturbation when we have such prior information.

Keywords

Differential privacy Utility Points of interest Geo-location data Laplacian noise 

Notes

Acknowledgement

We would like to thank Dr. Aleksandra Korolova for being the guiding light throughout the course of this paper.

References

  1. 1.
    Bindschaedler, V., Shokri, R.: Synthesizing plausible privacy preserving location traces. IEEE, August 2016Google Scholar
  2. 2.
    Andrés, M., Bordenable, N.E., Chatzikokolakis, K., Palamidessi, C.: Geo-indistinguishability: differential privacy for location-based systems. Springer, Switzerland (2015)Google Scholar
  3. 3.
    Andreś, M., Bordenable, N.E., Chatzikokolakis, K., Palamidessi, C.: Optimal geo-indistinguishable mechanisms for location privacy. In: Proceedings of the 2014 ACM SIGSAC, Conference on Computer and Communications SecurityGoogle Scholar
  4. 4.
  5. 5.
    Polakis, I., Argyros, G., Petsios, T., Sivakorn, S., Keromytis, A.D.: Where’s Wally? Precise user discovery attacks in location proximity services. In: Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security (2015)Google Scholar
  6. 6.
    Srivastava, V., Naik, V., Gupta, A.: Privacy breach of social relation from location based mobile applications. In: IEEE CS Home, pp. 324–328 (2014)Google Scholar
  7. 7.
    Liao, L., Fox, D., Kautz, H.: Extracting places and activities from GPS traces using hierarchical conditional random fields. Int. J. Robot. Res. Arch. 26(1), 119–134 (2007)CrossRefGoogle Scholar
  8. 8.
    Brenner, H., Nissim, K.: Impossibility of differentially private universally optimal mechanisms. In: 2010 51st Annual IEEE Symposium Foundations of Computer Science (FOCS)Google Scholar
  9. 9.
    Nunez, M., Frignal, J.: Geo–location inference attacks: from modelling to privacy risk assessment. In: EDCC 2014 Proceedings of the 2014 Tenth European Dependable Computing ConferenceGoogle Scholar
  10. 10.
    Gruteser, M., Grunwald, D.: Anonymous usage of location–based service through spatial and temporal cloaking. In: Proceeding MobiSys 2003 Proceedings of the 1st International Conference on Mobile Systems, Applications and ServicesGoogle Scholar
  11. 11.
    Kulik, L., Duckham, M.: A Formal Model of Obfuscation and Negotiation for Location Privacy. PERVASIVE Springer-Verlag, Heidelberg (2005)Google Scholar
  12. 12.
    Ardagna, C.A., Cremonini, M., Damiani, E., Samarati, P.: Location privacy protection through obfuscation–based techniques. In: IFIP Annual Conference on Data and Applications Security and Privacy DBSec 2007: Data and Applications SecurityGoogle Scholar
  13. 13.
    Chatzikokolakis, K., Elsalamouny, E., Palamidessi, C.: Practical Mechanisms for Location Privacy. Inria and LIX, cole PolytechniqueGoogle Scholar
  14. 14.
    ElSalamouny, E., Gambs, S.: Differential privacy models for location based services. Trans. Data Priv. 9, 15–48 (2016). INRIA, FranceGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of Southern CaliforniaLos AngelesUSA

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