World Wide Web

, Volume 18, Issue 1, pp 9–32 | Cite as

Neighborhood randomization for link privacy in social network analysis

Article

Abstract

Social network analysis has many important applications but it depends on sharing and publishing the underlying graph. Link privacy requires limiting the ability of an adversary to infer the presence of a sensitive link between two individuals in the published social network graph. A standard technique for achieving link privacy is to probabilistically randomize a link over the space for node pairs. A major drawback of such graph-wise randomization is that it ignores the structural proximity of nodes, thus, alters considerably the structure of social networks and distorts the accuracy of social network analysis. To address this problem, we propose a structure-aware randomization scheme, called neighborhood randomization. This scheme models a social network as a directed graph and probabilistically randomizes the destination of a link within a local neighborhood. By confining the randomization to a local neighborhood, this scheme drastically reduces the distortion to the graph structure yet hides a sensitive link. The trade-off between privacy and utility is dictated by the retention probability of a destination and by the size of the randomization neighborhood. We conduct extensive experiments to evaluate this trade-off using real life social network data.

Keywords

Social network analysis Privacy-preserving data publishing Link perturbation 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Agrawal, R., Srikant, R., Thomas, D.: Privacy preserving OLAP. In: SIGMOD (2005)Google Scholar
  2. 2.
    Backstrom, L., Dwork, C., Kleinberg, J.M.: Wherefore art thou R3579X?: anonymized social networks, hidden patterns, and structural steganography. In: WWW (2007)Google Scholar
  3. 3.
    Bai, K., Liu, Y., Liu, P.: Prevent identity disclosure in social network data study. In: ACM CCS (2009)Google Scholar
  4. 4.
    Barabasi, A.-L., Albert, R.: Emergence of scaling in random networks. Science 286, 509–512 (1999)CrossRefMathSciNetGoogle Scholar
  5. 5.
    Borgatti, S.P., Everett, M.G., Freeman, L.C.: Ucinet for Windows: Software for Social Network Analysis. Analytic Technologies, Harvard, MA (2002)Google Scholar
  6. 6.
    Brin, S., Page, L.: The anatomy of a large-scale hypertextual Web search engine. In: WWW (1998)Google Scholar
  7. 7.
    Campan, A., Truta, T.: A clustering approach for data and structural anonymity in social networks. In: PinKDD (2008)Google Scholar
  8. 8.
    Cheng, J., Fu, A.W., Liu, J.: K-isomorphism: privacy preserving network publication against structural attacks. In: SIGMOD (2010)Google Scholar
  9. 9.
    Evfimievski, A., Gehrke, J., Srikant, R.: Limiting privacy breaches in privacy preserving data mining. In: PODS (2003)Google Scholar
  10. 10.
    Fagin, R., Kumar, R., Sivakumar, D.: Comparing top k lists. SIAM J. Discret. Math. 17(1), 134–160 (2003)CrossRefMATHMathSciNetGoogle Scholar
  11. 11.
    Fung, B.C.M., Wang, K., Fu, A.W.-C., Yu, P.S.: Introduction to privacy-preserving data publishing: concepts and techniques. In: Data Mining and Knowledge Discovery Series. Chapman & Hall/CRC (2010)Google Scholar
  12. 12.
    Guimera, R., Danon, L., Guilera, A., Giralt, F., Arenas, A.: Self-similar community structure in a network of human interactions. Phys. Rev. 68, 065103 (2003)Google Scholar
  13. 13.
    Hay, M., Miklau, G., Jensen, D., Towsley, D., Weis, P.: Resisting structural reidentification in anonymized social networks. In: VLDB (2008)Google Scholar
  14. 14.
    Hay, M., Miklau, G., Jensen, D., Weis, P., Srivastava, S.: Anonymizing social networks. Technical report, University of Massachusetts Amherst (2007)Google Scholar
  15. 15.
    Liben-Nowell, D., Kleinberg, J.: The link prediction problem for social networks. In: CIKM (2003)Google Scholar
  16. 16.
    Litvak, N., Scheinhardt, W.R.W., Volkovich, Y.: In-degree and PageRank: why do they follow similar power laws? Internet Math. 4(2), 175–198 (2007)CrossRefMathSciNetGoogle Scholar
  17. 17.
    Liu, K., Terzi, E.: Towards identity anonymization on graphs. In: ACM SIGMOD/PODS (2008)Google Scholar
  18. 18.
    Medforth, N., Wang, K.: Privacy risk in graph stream publishing for social network data. In: ICDM (2011)Google Scholar
  19. 19.
    Milani Fard, A., Wang, K., Yu, P.S.: Limiting link disclosure in social network analysis through subgraph-wise perturbation. In: EDBT (2012)Google Scholar
  20. 20.
    Musiał, K., Kazienko, P.: Social networks on the Internet. WWWJ 16(1), 31–72 (2013)CrossRefGoogle Scholar
  21. 21.
    Newman, M.E.J.: The structure of scientific collaboration networks. In: Proc. of the National Academy of Sciences of the USA, vol. 98, issue 2 (2001)Google Scholar
  22. 22.
    Wasserman, S., Faust, K.: Social Network Analysis: Methods and Applications. Cambridge University Press (1999)Google Scholar
  23. 23.
    Wong, R.C., Fu, A.W., Wang, K., Pei, J.: Minimality attack in privacy preserving data publishing. In: VLDB (2007)Google Scholar
  24. 24.
    Ying, X., Wu, X.: Randomizing social networks: a spectrum preserving approach. In: SDM (2008)Google Scholar
  25. 25.
    Zhang, L., Zhang, W.: Edge anonymity in social network graphs. In: IEEE Social Computing (2009)Google Scholar
  26. 26.
    Zheleva, E., Getoor, L.: Preserving the privacy of sensitive relationships in graph data. In: PinKDD (2007)Google Scholar
  27. 27.
    Zhou, D., Pei, J.: Preserving privacy in social networks against neighborhood attacks. In: ICDE (2008)Google Scholar
  28. 28.
    Zou, L., Chen, L., Ozsu, M.T.: K-automorphism: a general framework for privacy preserving network publication. In: VLDB (2009)Google Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.School of Computing ScienceSimon Fraser UniversityBurnabyCanada
  2. 2.University of British ColumbiaVancouverCanada

Personalised recommendations