Big Brother Knows Your Friends: On Privacy of Social Communities in Pervasive Networks

  • Igor Bilogrevic
  • Murtuza Jadliwala
  • István Lám
  • Imad Aad
  • Philip Ginzboorg
  • Valtteri Niemi
  • Laurent Bindschaedler
  • Jean-Pierre Hubaux
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7319)


Wireless network operators increasingly deploy WiFi hotspots and low-power, low-range base stations in order to satisfy users’ growing demands for context-aware services and performance. In addition to providing better service, such capillary infrastructure deployment threatens users’ privacy with respect to their social ties and communities, as it allows infrastructure owners to infer users’ daily social encounters with increasing accuracy, much to the detriment of their privacy. Yet, to date, there are no evaluations of the privacy of communities in pervasive wireless networks. In this paper, we address the important issue of privacy in pervasive communities by experimentally evaluating the accuracy of an adversary-owned set of wireless sniffing stations in reconstructing the communities of mobile users. During a four-month trial, 80 participants carried mobile devices and were eavesdropped on by an adversarial wireless mesh network on a university campus. To the best of our knowledge, this is the first study that focuses on the privacy of communities in a deployed pervasive network and provides important empirical evidence on the accuracy and feasibility of community tracking in such networks.


Weight Function Mobile Device Community Detection Receive Signal Strength Indicator Community Statistic 
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.
    Gruteser, M., Hoh, B.: On the anonymity of periodic location samples. Security in Perv. Comp. (2005)Google Scholar
  2. 2.
    Hoh, B., Gruteser, M., Xiong, H., Alrabady, A.: Enhancing security and privacy in traffic-monitoring systems. IEEE Perv. Comp. 5 (2006)Google Scholar
  3. 3.
    Matsuo, Y., Okazaki, N., Izumi, K., Nakamura, Y., Nishimura, T., Hasida, K.: Inferring Long-term User Property based on Users. In: IJCAI (2007)Google Scholar
  4. 4.
    Noulas, A., Musolesi, M., Pontil, M., Mascolo, C.: Inferring interests from mobility and social interactions. In: NIPS Workshop on Analyzing Netw. and Learning w. Graphs (2009)Google Scholar
  5. 5.
    Crandall, D., Backstrom, L., Cosley, D., Suri, S., Huttenlocher, D., Kleinberg, J.: Inferring social ties from geographic coincidences. Proc. Nat. Academy of Sciences 107 (2010)Google Scholar
  6. 6.
    Mardenfeld, S., Boston, D., Pan, S., Jones, Q., Iamntichi, A., Borcea, C.: Gdc: Group discovery using co-location traces. In: Int. Conf. on Social Comp. (2010)Google Scholar
  7. 7.
  8. 8.
  9. 9.
    Corson, M., Laroia, R., Li, J., Park, V., Richardson, T., Tsirtsis, G.: Toward Proximity-aware Internetworking. Wireless Communications (2010)Google Scholar
  10. 10.
    Reeves, S.: Internet is double-edged sword in arab revolts (2011),
  11. 11.
    Follman, M.: “Bluetoothing” Iran’s revolution. (2010)Google Scholar
  12. 12.
    Zhang, D., Guo, B., Li, B., Yu, Z.: Extracting social and community intelligence from digital footprints: An emerging research area. Ubiq. Intell. and Computing (2010)Google Scholar
  13. 13.
    Hui, P., Chaintreau, A., Scott, J., Gass, R., Crowcroft, J., Diot, C.: Pocket switched networks and human mobility in conference environments. In: ACM SIGCOMM Workshop on DTN (2005)Google Scholar
  14. 14.
    Chaintreau, A., Hui, P., Crowcroft, J., Diot, C., Gass, R., Scott, J.: Impact of human mobility on opportunistic forwarding algorithms. IEEE TMC (2007)Google Scholar
  15. 15.
    Hossmann, T., Spyropoulos, T., Legendre, F.: Know thy neighbor: Towards optimal mapping of contacts to social graphs for dtn routing. In: INFOCOM (2010)Google Scholar
  16. 16.
    Hui, P., Crowcroft, J., Yoneki, E.: Bubble rap: social-based forwarding in delay tolerant networks. IEEE TMC (2010)Google Scholar
  17. 17.
    Eagle, N., Pentland, A., Lazer, D.: Inferring friendship network structure by using mobile phone data. Proc. Nat. Academy of Sciences 106 (2009)Google Scholar
  18. 18.
    González, M., Herrmann, H., Kertész, J., Vicsek, T.: Community structure and ethnic preferences in school friendship networks. Physica A: Statistical Mechanics and its Applications 379 (2007)Google Scholar
  19. 19.
    Business Week: Facebook’s value tops; trails only google on web,
  20. 20.
  21. 21.
    Gong, N.-W., Laibowitz, M., Paradiso, J.A.: Dynamic Privacy Management in Pervasive Sensor Networks. In: de Ruyter, B., Wichert, R., Keyson, D.V., Markopoulos, P., Streitz, N., Divitini, M., Georgantas, N., Mana Gomez, A. (eds.) AmI 2010. LNCS, vol. 6439, pp. 96–106. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  22. 22.
    Henderson, T., Kotz, D., Abyzov, I.: The changing usage of a mature campus-wide wireless network. In: Int. Conf. on Mobile Comp. and Networking (2004)Google Scholar
  23. 23.
    Eagle, N., Pentland, A.: Reality mining: sensing complex social systems. Pers. and Ubiq. Computing 10 (2006)Google Scholar
  24. 24.
    Aad, I., Jadliwala, M., Bilogrevic, I., Niemi, V., Hubaux, J.P., Ginzboorg, P., Leppänen, K.: Nokia Instant Community at EPFL: a real-world large-scale wireless peer-to-peer trial. Technical Report EPFL-REPORT-170421 (2011)Google Scholar
  25. 25.
  26. 26.
    Dolev, D., Yao, A.: On the security of public key protocols. IEEE TIT 29 (1983)Google Scholar
  27. 27.
    Fortunato, S.: Community detection in graphs. Physics Reports 486 (2010)Google Scholar
  28. 28.
    Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning, 2nd edn. (2008)Google Scholar
  29. 29.
    Newman, M.: Fast algorithm for detecting community structure in networks. Physical Review E 69 (2004)Google Scholar
  30. 30.
    Palla, G., Derényi, I., Farkas, I., Vicsek, T.: Uncovering the overlapping community structure of complex networks in nature and society. Nature 435 (2005)Google Scholar
  31. 31.
    Bindschaedler, L., Jadliwala, M., Bilogrevic, I., Aad, I., Ginzboorg, P., Niemi, V., Hubaux, J.P.: Track Me If You Can: On the Effectiveness of Context-based Identifier Changes in Deployed Mobile Networks. In: NDSS (2012)Google Scholar
  32. 32.
    Palla, G., Barabási, A., Vicsek, T.: Quantifying social group evolution. Nature 446 (2007)Google Scholar
  33. 33.
    Xu, K., Yang, G., Li, V., Chan, S.: Detecting dynamic communities in opportunistic networks. In: ICUFN (2009)Google Scholar
  34. 34.
    Bose, A., Foh, C.: A practical path loss model for indoor wifi positioning enhancement. In: Int. Conf. on Inform., Comm. & Signal Proc. (2007)Google Scholar
  35. 35.
    Derényi, I., Palla, G., Vicsek, T.: Clique percolation in random networks. Physical Review Letters 94 (2005)Google Scholar
  36. 36.
    Jaccard, P.: Etude comparative de la distribution florale dans une portion des alpes et du jura (1901)Google Scholar
  37. 37.
    Beresford, A., Stajano, F.: Location privacy in pervasive computing. IEEE Perv. Comp. 2 (2003)Google Scholar
  38. 38.
    Hong, J., Landay, J.: An architecture for privacy-sensitive ubiquitous computing. In: Conf. on Mobile Systems, Applications, and Services (2004)Google Scholar
  39. 39.
    Jadliwala, M., Bilogrevic, I., Hubaux, J.-P.: Optimizing Mixing in Pervasive Networks: A Graph-Theoretic Perspective. In: Atluri, V., Diaz, C. (eds.) ESORICS 2011. LNCS, vol. 6879, pp. 548–567. Springer, Heidelberg (2011)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Igor Bilogrevic
    • 1
  • Murtuza Jadliwala
    • 2
  • István Lám
    • 5
  • Imad Aad
    • 3
  • Philip Ginzboorg
    • 4
  • Valtteri Niemi
    • 4
  • Laurent Bindschaedler
    • 1
  • Jean-Pierre Hubaux
    • 1
  1. 1.LCA1, EPFLLausanneSwitzerland
  2. 2.EECS DepartmentWichita State UniversityUSA
  3. 3.Nokia Research CenterLausanneSwitzerland
  4. 4.Nokia Research CenterHelsinkiFinland
  5. 5.Faculty of Electrical Engineering and InformaticsBMEHungary

Personalised recommendations