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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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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