Semi-Edge Anonymity: Graph Publication when the Protection Algorithm Is Available

  • Mingxuan Yuan
  • Lei Chen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7238)


With the popularity of social networks, the privacy issues related with social network data become more and more important. The connection information between users, as well as their sensitive attributes, should be protected. There are some proposals studying how to publish a privacy preserving graph. However, when the algorithm which generates the published graph is known by the attacker, the current protection models may still leak some connection information. In this paper, we propose a new protection model, “Semi-Edge Anonymity”, to protect both user’s sensitive attributes and connection information even when an attacker knows the publication algorithm. Moreover, any state-of-art tabular data protection techniques can be applied to Semi-Edge Anonymity model to protect sensitive attributes. We theoretically prove that on two utilities, the possible world size and the true edge ratio, the Semi-Edge Anonymity model outperforms any clustering based model which protects links. We further conduct extensive experiments on real data sets for two other utilities. The results show that our model also has better performance on these utilities than the clustering based models.


Simulated Annealing Algorithm Cluster Base Model Sensitive Attribute Super Node Social Network Data 
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.
    Backstrom, L., et al.: Wherefore art thou r3579x?: anonymized social networks, hidden patterns, and structural steganography. In: WWW (2007)Google Scholar
  2. 2.
    Cormode, G., et al.: Class-based graph anonymization for social network data. In: VLDB (2009)Google Scholar
  3. 3.
    Bhagat, S., et al.: Prediction promotes privacy in dynamic social networks. In: WOSN (2010)Google Scholar
  4. 4.
    Campan, A., et al.: A clustering approach for data and structural anonymity in social networks. In: PinKDD (2008)Google Scholar
  5. 5.
    Campan, A., et al.: P-sensitive k-anonymity with generalization constraints. In: TDP (2010)Google Scholar
  6. 6.
    Cheng, J., et al.: K-Isomorphism: Privacy Preserving Network Publication against Structural Attacks. In: SIGMOD 2010 (2010)Google Scholar
  7. 7.
    Cormode, G., et al.: Anonymizing bipartite graph data using safe groupings. In: PVLDB (2008)Google Scholar
  8. 8.
    Cormode, G., et al.: Anonymizing bipartite graph data using safe groupings. In: PVLDB (2008)Google Scholar
  9. 9.
    Hay, M., et al.: Resisting structural re-identification in anonymized social networks. In: PVLDB (2008)Google Scholar
  10. 10.
    Hay, M., et al.: Resisting structural re-identification in anonymized social networks. In: VLDBJ (2010)Google Scholar
  11. 11.
    Li, N., et al.: t-closeness: Privacy beyond k-anonymity and l-diversity. In: ICDE (2007)Google Scholar
  12. 12.
    Liu, K., et al.: Towards identity anonymization on graphs. In: SIGMOD (2008)Google Scholar
  13. 13.
    Machanavajjhala, A., et al.: L-diversity: Privacy beyond k-anonymity. In: TKDD (2007)Google Scholar
  14. 14.
    Sweeney, L., et al.: k-anonymity: a model for protecting privacy. In: FKBS (2002)Google Scholar
  15. 15.
    Wong, R., et al.: Minimality attack in privacy preserving data publishing. In: VLDB (2007)Google Scholar
  16. 16.
    Xiao, X., et al.: Anatomy: Simple and effective privacy preservation. In: VLDB (2006)Google Scholar
  17. 17.
    Ying, X., et al.: Randomizing social networks: a spectrum preserving approach. In: SDM (2008)Google Scholar
  18. 18.
    Zheleva, E., Getoor, L.: Preserving the Privacy of Sensitive Relationships in Graph Data. In: Bonchi, F., Malin, B., Saygın, Y. (eds.) PInKDD 2007. LNCS, vol. 4890, pp. 153–171. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  19. 19.
    Zhou, B., et al.: Preserving privacy in social networks against neighborhood attacks. In: ICDE (2008)Google Scholar
  20. 20.
    Zhou, B., et al.: The k-anonymity and l-diversity approaches for privacy preservation in social networks against neighborhood attacks. In: KAIS (2010)Google Scholar
  21. 21.
    Zou, L., et al.: K-Automorphism: A General Framework For Privacy Preserving Network Publication. In: VLDB (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Mingxuan Yuan
    • 1
  • Lei Chen
    • 1
  1. 1.Department of Computer Science and EngineeringThe Hong Kong University of Science & TechnologyHong kong

Personalised recommendations