A Knowledge Model Sharing Based Approach to Privacy-Preserving Data Mining
Transactions on Data Privacy
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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 edge-based 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 measure-based prediction methods with the low-rank approximation-based prediction in this paper.