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EWNI: Efficient Anonymization of Vulnerable Individuals in Social Networks

  • Frank Nagle
  • Lisa Singh
  • Aris Gkoulalas-Divanis
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7302)

Abstract

Social networks, patient networks, and email networks are all examples of graphs that can be studied to learn about information diffusion, community structure and different system processes; however, they are also all examples of graphs containing potentially sensitive information. While several anonymization techniques have been proposed for social network data publishing, they all apply the anonymization procedure on the entire graph. Instead, we propose a local anonymization algorithm that focuses on obscuring structurally important nodes that are not well anonymized, thereby reducing the cost of the overall anonymization procedure. Based on our experiments, we observe that we reduce the cost of anonymization by an order of magnitude while maintaining, and even improving, the accuracy of different graph centrality measures, e.g. degree and betweenness, when compared to another well known data publishing approach.

Keywords

Social Network Graph Property Vulnerable Individual Entire Graph Privacy Model 
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.

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References

  1. 1.
    Adamic, L., Glance, N.: The political blogosphere and the 2004 US Election. In: WWW 2005 Workshop on the Weblogging Ecosystem (2005)Google Scholar
  2. 2.
    Backstrom, L., Dwork, C., Kleinberg, J.: Wherefore art thou r3579x? anonymized social networks, hidden patterns, and structural steganography. In: WWW (2007)Google Scholar
  3. 3.
    Bhagat, S., Cormode, G., Krishnamurthy, B., Srivastava, D.: Class-based graph anonymization for social network data. In: VLDB (2009)Google Scholar
  4. 4.
    Bonchi, F., Gionis, A., Tassa, T.: Identity obfuscation in graphs through the information theoretic lens. In: ICDE (2011)Google Scholar
  5. 5.
    Campan, A., Truta, T.: Anonymization of centralized and distributed social networks by sequential clusterings. In: PinKDD (2008)Google Scholar
  6. 6.
    Cheng, J., Fu, A., Liu, J.: K-isomorphism: privacy preserving network publication against structural attacks. In: SIGMOD (2010)Google Scholar
  7. 7.
    Das, S., Egecioglu, O., Abbadi, A.: Anonymizing weighted social network graphs. In: ICDE (2010)Google Scholar
  8. 8.
    Gleiser, P., Danon, L.: List of edges of the network of jazz musicians. Adv. Complex Systems 6, 565 (2003)CrossRefGoogle Scholar
  9. 9.
    Guimera, R., Danon, L., Diaz-Guilera, A., Giralt, F., Arenas, A.: Network of emal interchanges. Physical Review E 68 (2003)Google Scholar
  10. 10.
    Hanhijarvi, S., Garriga, G., Puolamaki, K.: Randomization techniques for graphs. In: SDM (2009)Google Scholar
  11. 11.
    Hay, M., Miklau, G., Jensen, D.: Enabling accurate analysis of private network data. Chapman & Hall, CRC Press (2010)Google Scholar
  12. 12.
    Hay, M., Miklau, G., Jensen, D., Towsley, D.: Resisting structural re-identification in anonymized social networks. In: VLDB (2008)Google Scholar
  13. 13.
    Hay, M., Miklau, G., Jensen, D., Weis, P., Srivastava, S.: Anonymizing social networks. Technical Report 19, University of Massachusetts (2007)Google Scholar
  14. 14.
    LeFevre, K., Terzi, E.: Grass: Graph structure summarization. In: SDM (2010)Google Scholar
  15. 15.
    Leskovec, J., Huttenlocher, D., Kleinberg, J.: Signed networks in social media. In: ACM Conference on Human Factors in Computing Systems (2010)Google Scholar
  16. 16.
    Liu, K., Terzi, E.: Towards identity anonymization on graphs. In: SIGMOD (2008)Google Scholar
  17. 17.
    Narayanan, A., Shmatikov, V.: De-anonymizing social networks. In: IEEE Symposium on Security and Privacy (2009)Google Scholar
  18. 18.
    Singh, L., Zhan, J.: Measuring topological anonymity in social networks. In: IEEE Conference on Granular Computing (2007)Google Scholar
  19. 19.
    Tai, C.-H., Yu, P.S., Yang, D.-N., Chen, M.-S.: Privacy-preserving social network publication against friendship attacks. In: KDD (2011)Google Scholar
  20. 20.
    Tassa, T., Cohen, D.: Anonymization of centralized and distributed social networks by sequential clusterings. In: TKDE (2011)Google Scholar
  21. 21.
    Wasserman, S., Faust, K.: Social network analysis: methods and applications. Cambridge University Press, Cambridge (1994)Google Scholar
  22. 22.
    Wu, W., Xiao, Y., Wang, W., He, Z., Wang, Z.: k-symmetry model for identity anonymization in social networks. In: EDBT (2010)Google Scholar
  23. 23.
    Ying, X., Wu, X.: Randomizing social networks: a spectrum preserving approach. In: SDM (2008)Google Scholar
  24. 24.
    Zheleva, E., Getoor, L.: Preserving the privacy of sensitive relationships in graph data. In: KDD 2007 Workshop on Privacy, Security, and Trust (2007)Google Scholar
  25. 25.
    Zhou, B., Pei, J.: Preserving privacy in social networks against neighborhood attacks. In: ICDE (2008)Google Scholar
  26. 26.
    Zou, L., Chen, L., Ozsu, M.: KAutomorphism: A general framework for privacy preserving network publication. In: VLDB (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Frank Nagle
    • 1
  • Lisa Singh
    • 1
  • Aris Gkoulalas-Divanis
    • 2
  1. 1.Georgetown UniversityUSA
  2. 2.IBM Research-ZürichRüschlikonSwitzerland

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