Privacy Preserving Social Network Publication on Bipartite Graphs

  • Jian Zhou
  • Jiwu Jing
  • Ji Xiang
  • Lei Wang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7322)


In social networks, some data may come in the form of bipartite graphs, where properties of nodes are public while the associations between two nodes are private and should be protected. When publishing the above data, in order to protect privacy, we propose to adopt the idea generalizing the graphs to super-nodes and super-edges. We investigate the problem of how to preserve utility as much as possible and propose an approach to partition the nodes in the process of generalization. Our approach can give privacy guarantees against both static attacks and dynamic attacks, and at the same time effectively answer aggregate queries on published data.


data publishing privacy preservation bipartite graph generalization 


  1. 1.
    Cormode, G., Srivastava, D., Yu, T., Zhang, Q.: Anonymizing bipartite graph data using safe groupings. The VLDB Journal 19, 115–139 (2010)CrossRefGoogle Scholar
  2. 2.
    Machanavajjhala, A., Kifer, D., Gehrke, J., Venkitasubramaniam, M.: l-diversity: Privacy beyond k-anonymity. ACM Transactions on Knowledge Discovery from Data (TKDD) 1, 3 (2007)CrossRefGoogle Scholar
  3. 3.
    Hay, M., Miklau, G., Jensen, D., Towsley, D., Li, C.: Resisting structural re-identification in anonymized social networks. The VLDB Journal 19, 797–823 (2010)CrossRefGoogle Scholar
  4. 4.
    Massa, P., Avesani, P.: Trust-aware bootstrapping of recommender systems. In: ECAI, vol. 6, pp. 29–33. Citeseer (2006)Google Scholar
  5. 5.
    Bhagat, S., Cormode, G., Krishnamurthy, B., Srivastava, D.: Class-based graph anonymization for social network data. Proceedings of the VLDB Endowment 2, 766–777 (2009)Google Scholar
  6. 6.
    Sweeney, L.: k-anonymity: A model for protecting privacy. International Journal on Uncertainty Fuzziness and Knowledgebased Systems 10, 557–570 (2002)MathSciNetMATHCrossRefGoogle Scholar
  7. 7.
    Zhou, B., Pei, J.: Preserving privacy in social networks against neighborhood attacks. In: Proceedings of the 2008 IEEE 24th International Conference on Data Engineering, pp. 506–515. IEEE Computer Society, Washington, DC (2008)CrossRefGoogle Scholar
  8. 8.
    Zou, L., Chen, L., Özsu, M.T.: k-automorphism: a general framework for privacy preserving network publication. Proc. VLDB Endow. 2, 946–957 (2009)Google Scholar
  9. 9.
    Cheng, J., Fu, A., Liu, J.: K-isomorphism: privacy preserving network publication against structural attacks. In: Proceedings of the 2010 International Conference on Management of Data, pp. 459–470. ACM (2010)Google Scholar
  10. 10.
    Hanhijärvi, S., Garriga, G.C., Puolamäki, K.: Randomization techniques for graphs. In: Proceedings of the 9th SIAM International Conference on Data Mining, SDM 2009, pp. 780–791 (2009)Google Scholar
  11. 11.
    Ying, X., Wu, X.: Randomizing social networks: a spectrum preserving approach. In: SDM, 739–750 (2008)Google Scholar
  12. 12.
    Ying, X., Wu, X.: Graph generation with prescribed feature constraints. In: SDM, pp. 966–977 (2009)Google Scholar
  13. 13.
    Russell, S., Norvig, P.: Artificial intelligence: A modern approach. Section15 (2003)Google Scholar
  14. 14.
    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
  15. 15.
    Liu, L., Wang, J., Liu, J., Zhang, J.: Privacy preservation in social networks with sensitive edge weights. In: 2009 SIAM International Conference on Data Mining (SDM 2009), Sparks, Nevada, pp. 954–965 (2009)Google Scholar
  16. 16.
    Das, S., Egecioglu, Ö., El Abbadi, A.: Anonymizing edge-weighted social network graphs. Computer Science, UC Santa Barbara, Tech. Rep. CS-2009-03 (2009)Google Scholar

Copyright information

© IFIP International Federation for Information Processing 2012

Authors and Affiliations

  • Jian Zhou
    • 1
  • Jiwu Jing
    • 1
  • Ji Xiang
    • 1
  • Lei Wang
    • 1
  1. 1.The State Key Laboratory of Information SecurityGraduate University of Chinese Academy of SciencesChina

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