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. © Springer-Verlag Berlin Heidelberg 2006.
CITATION STYLE
He, J., Chu, W. W., & Liu, Z. (2006). Inferring privacy information from social networks. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3975 LNCS, pp. 154–165). Springer Verlag. https://doi.org/10.1007/11760146_14
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