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Message Passing Based Privacy Preserve in Social Networks

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4 Author(s)
Kelin Xiang ; Coll. of Comput. Sci. & Technol., Zhejiang Univ. Hangzhou, Hangzhou, China ; Wei Luo ; Xingjian Lu ; Jianwei Yin

Although a lot of literatures have been proposed on the issue of privacy preserve with relational data, social networks bring new challenges of resisting re-identify attacks. Based on message passing, an approach of privacy preserve in social networks is proposed in this paper. Individuals are assigned to different clusters according to their quasi-identifies and structural similarity measured by message passing. With clusters, k-anonymous mask networks are achieved where any individual is indistinguishable to other k-1 individuals. The experiments show our approach can protect individuals'privacy effectively in social networks with little information loss during generalization.

Published in:

2012 Fourth International Conference on Multimedia Information Networking and Security

Date of Conference:

2-4 Nov. 2012