Intelligence and Security Informatics pp 249-273

Part of the Studies in Computational Intelligence book series (SCI, volume 135) | Cite as

Protecting Private Information in Online Social Networks

  • Jianming He
  • Wesley W. Chu


Because personal information can be inferred from associations with friends, privacy becomes increasingly important as online social network services gain more popularity. Our recent study showed that the causal relations among friends in social networks can be modeled by a Bayesian network, and personal attribute values can be inferred with high accuracy from close friends in the social network. Based on these insights, we propose schemes to protect private information by selectively hiding or falsifying information based on the characteristics of the social network. Both simulation results and analytical studies reveal that selective alterations of the social network (relations and/or attribute values) according to our proposed protection rule are much more effective than random alterations.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Abadi, M., Needham, R.: Prudent Engineering Practice for Cryptographic Protocols. Transactions on Software Engineering 22, 6–15 (1995)CrossRefGoogle Scholar
  2. 2.
    Bellovin, S.M., Merritt, M.: Encrypted Key Exchange: Password-Based Protocols Secure Against Dictionary Attacks. In: IEEE Symposium on Security and Privacy, Oakland, California, May 1992, pp. 72–84 (1992)Google Scholar
  3. 3.
    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
  4. 4.
    Domingos, P., Richardson, M.: Mining the Network Value of Customers. In: Proceedings of the Seventh International Conference on Knowledge Discovery and Data Mining (2001)Google Scholar
  5. 5.
    Doreian, P.: Models of Network Effects on Social Actors. In: Freeman, L.C., White, D.R., Romney, K. (eds.) Research Methods in Social Analysis, pp. 295–317. George Mason University Press, Fairfax (1989)Google Scholar
  6. 6.
    Epinions (1999),
  7. 7.
    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 (1999)Google Scholar
  8. 8.
    He, J., Chu, W.W., Liu, Z.: Inferring Privacy Information from Social Networks. In: Mehrotra, S., Zeng, D.D., Chen, H., Thuraisingham, B., Wang, F.-Y. (eds.) ISI 2006. LNCS, vol. 3975, Springer, Heidelberg (2006)CrossRefGoogle Scholar
  9. 9.
    Heckerman, D.: A Tutorial on Learning Bayesian Networks. Technical Report. MSR-TR-95-06 (1995)Google Scholar
  10. 10.
    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
  11. 11.
    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
  12. 12.
    Livejournal (1997),
  13. 13.
    Leenders, R.T.: Modeling Social Influence Through Network Autocorrelation: Constructing the Weight Matrix. Social Networks 24, 21–47 (2002)CrossRefGoogle Scholar
  14. 14.
    Lowd, D., Domingos, P.: Naive Bayes Models for Probability Estimation. In: Proceedings of the Twenty-Second International Conference on Machine Learning (ICML). ACM Press, Bonn (2005)Google Scholar
  15. 15.
    MacQueen, J.B.: Some Methods for Classification and Analysis of Multivariate Observations. In: Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability, vol. 1, pp. 281–297. University of California Press, Berkeley (1967)Google Scholar
  16. 16.
    Milgram, S.: The Small World Problem. Psychology Today (1967)Google Scholar
  17. 17.
    Newman, M.E.: The Structure and Function of Complex Networks. SIAM Review 45(2), 167–256 (2003)MATHCrossRefMathSciNetGoogle Scholar
  18. 18.
    Sweeney, L.: K-Anonymity: A Model for Protecting Privacy. International Journal on Uncertainty, Fuzziness, and Knowledge-Based Systems 10(5) (2002)Google Scholar
  19. 19.
    U.D. of Health and O. for Civil Rights Human Services, Standards for Privacy of Individually Identifiable Health Information (2003),
  20. 20.
    Watts, D.J., Strogatz, S.H.: Collective Dynamics of Small-World Networks. Nature (1998)Google Scholar
  21. 21.
    W.W.W.C. (W3C), The Platform for Privacy Preferences 1.1 (2004),
  22. 22.
    Zhang, N.L., Poole, D.: Exploiting Causal Independence in Bayesian Network Inference. Journal of Artificial Intelligence Research 5, 301–328 (1996)MATHMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Jianming He
    • 1
  • Wesley W. Chu
    • 1
  1. 1.Computer Science DepartmentUniversity of CaliforniaUSA

Personalised recommendations