The Impact of Unlinkability on Adversarial Community Detection: Effects and Countermeasures

  • Shishir Nagaraja
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6205)


We consider the threat model of a mobile-adversary drawn from contemporary computer security literature, and explore the dynamics of community detection and hiding in this setting. Using a real-world social network, we examine the extent of network topology information an adversary is required to gather in order to accurately ascertain community membership information. We show that selective surveillance strategies can improve the adversary’s efficiency over random wiretapping. We then consider possible privacy preserving defenses; using anonymous communications helps, but not much; however, the use of counter-surveillance techniques can significantly reduce the adversary’s ability to learn community membership. Our analysis shows that even when using anonymous communications an adversary placing a selectively chosen 8% of the nodes of this network under surveillance (using key-logger probes) can de-anonymize the community membership of as much as 50% of the network. Uncovering all community information with targeted selection requires probing as much as 75% of the network. Finally, we show that a privacy conscious community can substantially disrupt community detection using only local knowledge even while facing up to the asymmetry of a completely knowledgeable mobile-adversary.


Social Network Betweenness Centrality Community Detection Threat Model Community Detection Algorithm 
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.
    Abello, J., Resende, M.G., Sudarsky, S.: Massive quasi-clique detection. In: Rajsbaum, S. (ed.) LATIN 2002. LNCS, vol. 2286, p. 598. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  2. 2.
    Becchetti, L., Boldi, P., Castillo, C., Gionis, A.: Efficient semi-streaming algorithms for local triangle counting in massive graphs. In: KDD (2008)Google Scholar
  3. 3.
    Bonacich, P.: Power and centrality: A family of measures. The American Journal of Sociology 92(5), 1170–1182 (1987)CrossRefGoogle Scholar
  4. 4.
    Brandes, U.: A faster algorithm for betweenness centrality. Journal of Mathematical Sociology 25(2), 163–177 (2001)CrossRefzbMATHGoogle Scholar
  5. 5.
    Chakrabarti, D., Papadimitriou, S., Modha, D., Faloutsos, C.: Fully automatic cross-associations. In: KDD (2004)Google Scholar
  6. 6.
    Chang, J., Blei, D.M.: Hierarchical relational models for document networks (2009)Google Scholar
  7. 7.
    Clauset, A., Newman, M.E.J., Moore, C.: Finding community structure in very large networks (August 2004)Google Scholar
  8. 8.
    Danezis, G., Dingledine, R., Mathewson, N.: Mixminion: Design of a type iii anonymous remailer protocol. In: IEEE Symposium on Security and Privacy, pp. 2–15 (2003)Google Scholar
  9. 9.
    Danezis, G., Wittneben, B.: The economics of mass surveillance and the questionable value of anonymous communications. In: Anderson, R. (ed.) Proceedings of the Fifth Workshop on the Economics of Information Security (WEIS 2006), Cambridge, UK (June 2006)Google Scholar
  10. 10.
    Dingledine, R., Mathewson, N., Syverson, P.: Tor: The second-generation onion router. In: Proceedings of the 13th USENIX Security Symposium (August 2004)Google Scholar
  11. 11.
    Dumais, S., Platt, J., Heckerman, D., Sahami, M.: Inductive learning algorithms and representations for text categorization. In: CIKM 1998: Proceedings of the seventh international conference on Information and knowledge management, pp. 148–155. ACM, New York (1998)CrossRefGoogle Scholar
  12. 12.
    Elsner, U.: Graph partitioning - a survey. MONARCH - Dokumenten- und Publikationsservice (2005), (German)
  13. 13.
    Fortunato, S., Latora, V., Marchiori, M.: Method to find community structures based on information centrality. Physical Review E 70(5) (2004)Google Scholar
  14. 14.
    Fortunato, S.: Community detection in graphs. arxiv eprint 0906.0612 (January 2010),
  15. 15.
    Freeman, L.C.: Centrality in social networks: Conceptual clarification. Social Networks 1, 215–239 (1978)CrossRefGoogle Scholar
  16. 16.
    Gibson, D., Kumar, R., Tomkins, A.: Discovering large dense subgraphs in massive graphs. In: VLDB (2005)Google Scholar
  17. 17.
    Gionis, A., Mannila, H., Seppänen, J.K.: Geometric and combinatorial tiles in 0–1 data. In: Boulicaut, J.-F., Esposito, F., Giannotti, F., Pedreschi, D. (eds.) PKDD 2004. LNCS (LNAI), vol. 3202, pp. 173–184. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  18. 18.
    Gu, G., Perdisci, R., Zhang, J., Lee, W.: BotMiner: Clustering analysis of network traffic for protocol- and structure-independent botnet detection. In: Proceedings of the 17th USENIX Security Symposium, Security 2008 (2008)Google Scholar
  19. 19.
    Guimerà, R., Danon, L., Díaz-Guilera, A., Giralt, F., Arenas, A.: Self-similar community structure in a network of human interactions. Phys. Rev. E 68(6), 065103 (2003)CrossRefGoogle Scholar
  20. 20.
    Kannan, R., Vempala, S., Vetta, A.: On clusterings: Good, bad and spectral. J. ACM 51(3), 497–515 (2004)MathSciNetCrossRefzbMATHGoogle Scholar
  21. 21.
    Kernighan, B.W., Lin, S.: An efficient heuristic procedure for partitioning graphs. Bell Systems Technology J. 49(2), 292–370 (1970)CrossRefzbMATHGoogle Scholar
  22. 22.
    Kumar, R., Raghavan, P., Rajagopalan, S., Tomkins, A.: Trawling the web for emerging cyber-communities. In: WWW (1999)Google Scholar
  23. 23.
    Latora, V., Marchiori, M.: Economic small-world behavior in weighted networks. The European Physical Journal B - Condensed Matter 32(2) (2002)Google Scholar
  24. 24.
    Leskovec, J., Lang, K.J., Dasgupta, A., Mahoney, M.W.: Statistical properties of community structure in large social and information networks. In: WWW (2008)Google Scholar
  25. 25.
    McCallum, A.K.: Mallet: A machine learning for language toolkit (2002),
  26. 26.
    Nagaraja, S., Anderson, R.: The topology of covert conflict. In: Moore, T. (ed.) Pre-Proceedings of The Fifth Workshop on the Economics of Information Security (June 2006)Google Scholar
  27. 27.
    Nagaraja, S., Anderson, R.: The snooping dragon: social-malware surveillance of the tibetan movement. Technical Report UCAM-CL-TR-746, University of Cambridge (March 2009)Google Scholar
  28. 28.
    Newman, M.: Modularity and community structure in networks. PNAS 103(23), 8577–8582 (2006)CrossRefGoogle Scholar
  29. 29.
    Newman, M., Girvan, M.: Finding and evaluating community structure in networks. Physical Review E (Statistical, Nonlinear, and Soft Matter Physics) 69(2) (2004)Google Scholar
  30. 30.
    Ostrovsky, R., Yung, M.: How to withstand mobile virus attacks (extended abstract). In: PODC 1991: Proceedings of the tenth annual ACM symposium on Principles of distributed computing, pp. 51–59. ACM, New York (1991)CrossRefGoogle Scholar
  31. 31.
    Pei, J., Jiang, D., Zhang, A.: On mining cross-graph quasi-cliques. In: ACM KDD Conference (2005)Google Scholar
  32. 32.
    Pfitzmann, A., Hansen, M.: Anonymity, unobservability, and pseudonymity: A consolidated proposal for terminology. Draft (July 2000)Google Scholar
  33. 33.
    Reed, M.G., Syverson, P.F., Goldschlag, D.M.: Anonymous connections and onion routing. IEEE Journal on Selected Areas in Communications 16(4) (1998)Google Scholar
  34. 34.
    Reiter, M.K., Rubin, A.D.: Crowds: anonymity for web transactions. ACM Trans. Inf. Syst. Secur. 1(1), 66–92 (1998)CrossRefGoogle Scholar
  35. 35.
    Satulouri, V., Parthasarathy, S.: Scalable graph clustering using stochastic flows: Applications to community discovery. In: KDD Conference (2009)Google Scholar
  36. 36.
    Tang, W., Liu, H.: Graph mining applications to social network analysis. In: Aggarwal, C., Wang, H. (eds.) Managing and Mining Graph Data (2010)Google Scholar
  37. 37.
    Yu, H., Kaminsky, M., Gibbons, P., Flaxman, A.: Sybilguard: Defending against sybil attacks via social networks. In: SIGCOMM (2006)Google Scholar
  38. 38.
    Zeng, Z., Wang, J., Zhou, L., Karypis, G.: Out-of-core coherent closed quasi-clique mining from large dense graph databases. ACM Transactions on Database Systems 31(2) (2007)Google Scholar
  39. 39.
    Zhao, Y., Karypis, G., Fayyad, U.: Hierarchical clustering algorithms for document datasets. Data Min. Knowl. Discov. 10(2), 141–168 (2005)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Shishir Nagaraja
    • 1
  1. 1.University of Illinois at Urbana-ChampaignUrbanaUSA

Personalised recommendations