Inferring privacy information from social networks

Authors:
Jianming He;Wesley W. Chu;Zhenyu (Victor) Liu
Affiliations:
Computer Science Department, UCLA, Los Angeles, CA;Computer Science Department, UCLA, Los Angeles, CA;Google Inc.
Venue:
ISI'06 Proceedings of the 4th IEEE international conference on Intelligence and Security Informatics
Year:
2006

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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.