Evaluating the Privacy Risk of Location-Based Services

  • Julien Freudiger
  • Reza Shokri
  • Jean-Pierre Hubaux
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7035)


In modern mobile networks, users increasingly share their location with third-parties in return for location-based services. Previous works show that operators of location-based services may identify users based on the shared location information even if users make use of pseudonyms. In this paper, we push the understanding of the privacy risk further. We evaluate the ability of location-based services to identify users and their points of interests based on different sets of location information. We consider real life scenarios of users sharing location information with location-based services and quantify the privacy risk by experimenting with real-world mobility traces.


Mobile User Location Privacy Work Location User Privacy Privacy Risk 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Aki, A.: The discovery of a lifetime.,
  2. 2.
    Ashbrook, D., Starner, T.: Using GPS to learn significant locations and predict movement across multiple users. Personal and Ubiquitous Computing 7(5), 275–286 (2003)CrossRefGoogle Scholar
  3. 3.
    Barnes, R., Cooper, A., Sparks, R., Jennings, C.: IETF geographic location/privacy,
  4. 4.
    Beresford, A.R., Stajano, F.: Location privacy in pervasive computing. Pervasive Computing, IEEE 2(1), 46–55 (2003)CrossRefGoogle Scholar
  5. 5.
    Beresford, A.R., Stajano, F.: Mix zones: User privacy in location-aware services. In: PerSec (March 2004)Google Scholar
  6. 6.
    Bettini, C., Wang, X.S., Jajodia, S.: Protecting Privacy Against Location-Based Personal Identification. In: Jonker, W., Petković, M. (eds.) SDM 2005. LNCS, vol. 3674, pp. 185–199. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  7. 7.
    Chaintreau, A., Hui, P., Crowcroft, J., Diot, C., Gass, R., Scott, J.: Impact of human mobility on opportunistic forwarding algorithms. IEEE TMC 6, 606–620 (2007)Google Scholar
  8. 8.
    Cloudmade. Makes maps differently,
  9. 9.
    Dalenius, T.: Finding a needle in a haystack - or identifying anonymous census records. Journal of Official Statistics 2(3), 329–336 (1986)Google Scholar
  10. 10.
    Díaz, C., Seys, S., Claessens, J., Preneel, B.: Towards Measuring Anonymity. In: Dingledine, R., Syverson, P.F. (eds.) PET 2002. LNCS, vol. 2482, pp. 54–68. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  11. 11.
    Eagle, N., Pentland, A., Lazer, D.: Inferring social network structure using mobile phone data. National Academy of Sciences (PNAS), 15274–15278 (2009)Google Scholar
  12. 12.
    Foursquare. Check-in, find your friends, unlock your city,
  13. 13.
    Frejinger, E.: Route choice analysis: data, models, algorithms and applications. PhD thesis, EPFL (2008)Google Scholar
  14. 14.
    Freudiger, J., Manshaei, M.H., Boudec, J.-Y.L., Hubaux, J.-P.: On the age of pseudonyms in mobile ad hoc networks. In: Infocom (2010)Google Scholar
  15. 15.
    Freudiger, J., Manshaei, M.H., Hubaux, J.-P., Parkes, D.C.: On non-cooperative location privacy: A game-theoretic analysis. In: CCS (2009)Google Scholar
  16. 16.
    Freudiger, J., Shokri, R., Hubaux, J.-P.: On the Optimal Placement of Mix Zones. In: Goldberg, I., Atallah, M.J. (eds.) PETS 2009. LNCS, vol. 5672, pp. 216–234. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  17. 17.
    Friedland, G., Sommer, R.: Cybercasing the joint: On the privacy implications of geo-tagging. In: HotSec (2010)Google Scholar
  18. 18.
    Golle, P., Partridge, K.: On the anonymity of home/work location pairs. In: Pervasive (2009)Google Scholar
  19. 19.
    Google Mobile Blog. Finding places “near me now” is easier & faster than ever (2010),
  20. 20.
    Gruteser, M., Grunwald, D.: Anonymous usage of location-based services through spatial and temporal cloaking. In: MobiSys (2003)Google Scholar
  21. 21.
    Hoh, B., Gruteser, M.: Protecting location privacy through path confusion. In: SECURECOMM (2005)Google Scholar
  22. 22.
    Hoh, B., Gruteser, M., Herring, R., Ban, J., Work, D., Herrera, J.-C., Bayen, A.M., Annavaram, M., Jacobson, Q.: Virtual trip lines for distributed privacy-preserving traffic monitoring. In: MobiSys (2008)Google Scholar
  23. 23.
    Hoh, B., Gruteser, M., Xiong, H., Alrabady, A.: Enhancing security and privacy in traffic-monitoring systems. Pervasive Computing, 38–46 (2006)Google Scholar
  24. 24.
    Hoh, B., Gruteser, M., Xiong, H., Alrabady, A.: Preserving privacy in GPS traces via uncertainty-aware path cloaking. In: CCS (2007)Google Scholar
  25. 25.
    Kiukkonen, N., Blom, J., Dousse, O., Gatica-Perez, D., Laurila, J.: Towards rich mobile phone datasets: Lausanne data collection campaign. In: ICPS (2010)Google Scholar
  26. 26.
    Krumm, J.: Inference attacks on location tracks. In: Pervasive (2007)Google Scholar
  27. 27.
    Kullback, S., Leibler, R.A.: On information and sufficiency. The Annals of Mathematical Statistics 22(1), 79–86 (1951)MathSciNetCrossRefzbMATHGoogle Scholar
  28. 28.
    Liao, L., Fox, D., Kautz, H.: Location-based activity recognition using relational Markov networks. In: IJCAI (2005)Google Scholar
  29. 29.
    Liao, L., Patterson, D.J., Fox, D., Kautz, H.: Learning and inferring transportation routines. Artificial Intelligence (171), 311–331 (2007)Google Scholar
  30. 30.
    Loopt. Discover the world around you,
  31. 31.
    Ma, C.Y.T., Yau, D.K.Y., Yip, N.K., Rao, N.S.V.: Privacy vulnerability of published anonymous mobility traces. In: MobiCom (2010)Google Scholar
  32. 32.
    Mokbel, M.F., Chow, C.-Y., Aref, W.G.: The new casper: Query processing for location services without compromising privacy. In: VLDB (2006)Google Scholar
  33. 33.
    Mulder, Y.D., Danezis, G., Batina, L., Preneel, B.: Identification via location-profiling in GSM networks. In: WPES (2008)Google Scholar
  34. 34.
    Narayanan, A., Shmatikov, V.: De-anonymizing social networks. In: Security and Privacy (2009)Google Scholar
  35. 35.
    Piorkowski, M.: Sampling urban mobility through on-line repositories of GPS tracks. In: HotPlanet (2009)Google Scholar
  36. 36.
    Piorkowski, M., Sarafijanovic-Djukic, N., Grossglauser, M.: A parsimonious model of mobile partitioned networks with clustering. In: ComsNets, pp. 1–10 (2009)Google Scholar
  37. 37.
    Roth, C., Kang, S.M., Batty, M., Barthelemy, M.: Commuting in a polycentric city. Technical report, CNRS (2010)Google Scholar
  38. 38.
    Serjantov, A., Danezis, G.: Towards an Information Theoretic Metric for Anonymity. In: Dingledine, R., Syverson, P.F. (eds.) PET 2002. LNCS, vol. 2482, pp. 41–53. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  39. 39.
    Shokri, R., Freudiger, J., Hubaux, J.-P.: A unified framework for location privacy. In: HotPETs (2010)Google Scholar
  40. 40.
    Shokri, R., Theodorakopoulos, G., Boudec, J.-Y.L., Hubaux, J.-P.: Quantifying Location Privacy. In: IEEE S&P (2011)Google Scholar
  41. 41.
    Sweeney, L.: k-anonymity: A model for protecting privacy. Uncertainty, Fuzziness and Knowledge-based systems 10, 557–570 (2002)MathSciNetCrossRefzbMATHGoogle Scholar
  42. 42.
    Vojnovic, M., Boudec, J.-Y.L.: Perfect simulation and stationarity of a class of mobility models. In: Infocom (2005)Google Scholar
  43. 43.
    Zhong, S., Li, L.E., Liu, Y.G., Yang, Y.R.: Privacy-preserving location-based services for mobile users in wireless networks. Technical report, SUNY at Buffalo (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Julien Freudiger
    • 1
  • Reza Shokri
    • 1
  • Jean-Pierre Hubaux
    • 1
  1. 1.LCA1EPFLSwitzerland

Personalised recommendations