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Title of Journal: Knowl Inf Syst

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Abbravation: Knowledge and Information Systems

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Springer-Verlag

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10.1007/bf00290319

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0219-3116

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On link privacy in randomizing social networks

Authors: Xiaowei Ying Xintao Wu
Publish Date: 2010/11/12
Volume: 28, Issue: 3, Pages: 645-663
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Abstract

Many applications of social networks require relationship anonymity due to the sensitive stigmatizing or confidential nature of relationship Recent work showed that the simple technique of anonymizing graphs by replacing the identifying information of the nodes with random IDs does not guarantee privacy since the identification of the nodes can be seriously jeopardized by applying subgraph queries In this paper we investigate how well an edgebased graph randomization approach can protect sensitive links We show via theoretical studies and empirical evaluations that various similarity measures can be exploited by attackers to significantly improve their confidence and accuracy of predicted sensitive links between nodes with high similarity values We also compare our similarity measurebased prediction methods with the lowrank approximationbased prediction in this paper

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