Inferring Privacy Information from Social Networks

  • Jianming He
  • Wesley W. Chu
  • Zhenyu (Victor) Liu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3975)


Since privacy information can be inferred via social relations, the privacy confidentiality problem becomes increasingly challenging as online social network services are more popular. Using a Bayesian network approach to model the causal relations among people in social networks, we study the impact of prior probability, influence strength, and society openness to the inference accuracy on a real online social network. Our experimental results reveal that personal attributes can be inferred with high accuracy especially when people are connected with strong relationships. Further, even in a society where most people hide their attributes, it is still possible to infer privacy information.


Social Network Bayesian Network Prior Probability Bayesian Inference Directed Acyclic Graph 
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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  1. 1.
  2. 2.
    Brin, S., Page, L.: The anatomy of a large-scale hypertextual Web search engine. In: Proceedings of the Seventh International World Wide Web Conference (1998)Google Scholar
  3. 3.
    Domingos, P., Richardson, M.: Mining the network value of customers. In: Proceedings of the 7th International Conference on Knowledge Discovery and Data Mining (2001)Google Scholar
  4. 4.
    Heckerman, D.: A Tutorial on Learning Bayesian Networks. Technical Report MSR-TR-95-06 (March 1995)Google Scholar
  5. 5.
    Heckerman, D., Geiger, D., Chickering, D.M.: Learning bayesian networks: The combination of knowledge and statistical data. In: KDD Workshop, pp. 85–96 (1994)Google Scholar
  6. 6.
    Kautz, H., Selman, B., Shah, M.: Referral Web: Combining social networks and collaborative filtering. Communications of the ACM 40(3), 63–65 (1997)CrossRefGoogle Scholar
  7. 7.
    Lowd, D., Domingos, P.: Naive bayes models for probability estimation. In: Proceedings of the Twenty-Second International Conference on Machine Learning (ICML), Bonn, Germany. ACM Press, New York (2005)Google Scholar
  8. 8.
    Milgram, S.: The small world problem. Psychology Today (1967)Google Scholar
  9. 9.
    Friedman, N., Getoor, L., Koller, D., Pfeffer, A.: Learning probabilistic relational models. In: Proceedings of the 16th International Joint Conference on Artificial Intelligence (IJCAI), Stockholm, Sweden (August 1999)Google Scholar
  10. 10.
    Newman, M.: The structure and function of complex networks. SIAM Review 45(2), 167–256 (2003)zbMATHCrossRefMathSciNetGoogle Scholar
  11. 11.
    U. D. of Health and O. for Civil Rights Human Services. Standards for Privacy of Individually Identifiable Health Information (2003),
  12. 12.
    W.W.W.C. (W3C). The platform for privacy preferences 1.1, P3P1.1 (2004),
  13. 13.
    Watts, D.J., Strogatz, S.H.: Collective dynamics of “small-world” networks. Nature (1998)Google Scholar
  14. 14.
    Zhang, N.L., Poole, D.: Exploiting causal independence in bayesian network inference. Journal of Artificial Intelligence Research 5, 301–328 (1996)zbMATHMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Jianming He
    • 1
  • Wesley W. Chu
    • 1
  • Zhenyu (Victor) Liu
    • 2
  1. 1.Computer Science DepartmentUCLALos AngelesUSA
  2. 2.Google Inc.USA

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