An Effective Method for Utility Preserving Social Network Graph Anonymization Based on Mathematical Modeling
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Abstract:
In recent years, privacy concerns about social network graph data publishing has increased due to the widespread use of such data for research purposes. This paper addresses the problem of identity disclosure risk of a node assuming that the adversary identifies one of its immediate neighbors in the published data. The related anonymity level of a graph is formulated and a mathematical model is proposed to solve the problem. The application of the method on a number of synthetic and real-world datasets confirms that the method is general and can be used in different contexts to produce superior results in terms of the utility of the anonymized graph.
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Journal title
volume 31 issue 10
pages 1624- 1632
publication date 2018-10-01
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