Cluster-Based Anonymization of Directed Graphs
2019 IEEE 5th International Conference on Collaboration and Internet Computing (CIC)(2019)
摘要
Social network providers anonymize graphs storing users' relationships to protect users from being re-identified. Despite the fact that most of the relationships are directed (e.g., follows), few works (e.g., the Paired-degree [1] and K-In&Out-Degree Anonymity [2]) have been designed to work with directed graphs. In this paper, we show that given a graph, DGA [1]and DSNDG-KIODA [2] are not always able to generate its anonymized version. We overcome this limitation by presenting the Cluster-based Directed Graph Anonymization Algorithm(CDGA) and prove that, by choosing the appropriate parameters, CDGA can generate an anonymized graph satisfying both the Paired k-degree [1] and K-In&Out-Degree Anonymity [2]. Also, we present the Out-and In-Degree Information Loss Metric to minimize the number of changes made to anonymize the graph. We conduct extensive experiments on three real-life data sets to evaluate the effectiveness of CDGA and compare the quality of the graphs anonymized by CDGA, DGA, and DSNDG-KIODA. The experimental results show that we can generate anonymized graphs, by modifying less than 0.007% of edges in the original graph.
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关键词
Anonymity,Directed Graphs,Privacy
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