Inferring Privacy Information Via Social Relations
2008 IEEE 24TH INTERNATIONAL CONFERENCE ON DATA ENGINEERING WORKSHOP, VOLS 1 AND 2(2008)
摘要
Currently, millions of individuals are sharing personal information and building social relations with others, through online social network sites. Recent research has shown that those personal information could compromise owners' privacy. In this work, we are interested in the privacy of online social network users with missing personal information. We study the problem of inferring those users' personal information via their social relations. We present an iterative algorithm, by combining a Bayesian label classification method and discriminative social relation choosing, for inferring personal information. Our experimental results reveal that personal information of most users in an online social network could be inferred through mere social relations with high accuracy.
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关键词
bipartite graph,computer science,privacy,beryllium,nitrogen,indium,iterative algorithm,social relation,arsenic,information analysis,bayesian methods,biomedical imaging,data privacy,iterative methods,accuracy,operations research,probability
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