Abstract
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.
This research is in part supported by NSF grant # IIS-03113283.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Live journal, http://www.livejournal.com
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)
Domingos, P., Richardson, M.: Mining the network value of customers. In: Proceedings of the 7th International Conference on Knowledge Discovery and Data Mining (2001)
Heckerman, D.: A Tutorial on Learning Bayesian Networks. Technical Report MSR-TR-95-06 (March 1995)
Heckerman, D., Geiger, D., Chickering, D.M.: Learning bayesian networks: The combination of knowledge and statistical data. In: KDD Workshop, pp. 85–96 (1994)
Kautz, H., Selman, B., Shah, M.: Referral Web: Combining social networks and collaborative filtering. Communications of the ACM 40(3), 63–65 (1997)
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)
Milgram, S.: The small world problem. Psychology Today (1967)
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)
Newman, M.: The structure and function of complex networks. SIAM Review 45(2), 167–256 (2003)
U. D. of Health and O. for Civil Rights Human Services. Standards for Privacy of Individually Identifiable Health Information (2003), http://www.hhs.gov/ocr/combinedregtext.pdf
W.W.W.C. (W3C). The platform for privacy preferences 1.1, P3P1.1 (2004), http://www.w3.org/TR/P3P11/
Watts, D.J., Strogatz, S.H.: Collective dynamics of “small-world” networks. Nature (1998)
Zhang, N.L., Poole, D.: Exploiting causal independence in bayesian network inference. Journal of Artificial Intelligence Research 5, 301–328 (1996)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
He, J., Chu, W.W., Liu, Z.(. (2006). Inferring Privacy Information from Social Networks. In: Mehrotra, S., Zeng, D.D., Chen, H., Thuraisingham, B., Wang, FY. (eds) Intelligence and Security Informatics. ISI 2006. Lecture Notes in Computer Science, vol 3975. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11760146_14
Download citation
DOI: https://doi.org/10.1007/11760146_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34478-0
Online ISBN: 978-3-540-34479-7
eBook Packages: Computer ScienceComputer Science (R0)