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Type: Journal article
Title: Anonymizing graphs against weight-based attacks with community preservation
Author: Li, Y.
Shen, H.
Citation: Journal of Computing Science and Engineering, 2011; 5(3):197-209
Publisher: Korean Institute of Information Scientists and Engineers
Issue Date: 2011
ISSN: 1976-4677
Statement of
Yidong Li and Hong Shen
Abstract: The increasing popularity of graph data, such as social and online communities, has initiated a prolific research area in knowledge discovery and data mining. As more real-world graphs are released publicly, there is growing concern about privacy breaching for the entities involved. An adversary may reveal identities of individuals in a published graph, with the topological structure and/or basic graph properties as background knowledge. Many previous studies addressing such attacks as identity disclosure, however, concentrate on preserving privacy in simple graph data only. In this paper, we consider the identity disclosure problem in weighted graphs. The motivation is that, a weighted graph can introduce much more unique information than its simple version, which makes the disclosure easier. We first formalize a general anonymization model to deal with weight-based attacks. Then two concrete attacks are discussed based on weight properties of a graph, including the sum and the set of adjacent weights for each vertex. We also propose a complete solution for the weight anonymization problem to prevent a graph from both attacks. In addition, we also investigate the impact of the proposed methods on community detection, a very popular application in the graph mining field. Our approaches are efficient and practical, and have been validated by extensive experiments on both synthetic and real-world datasets.
Keywords: Anonymity; weighted graph; privacy preserving graph mining; weight anonymization
Rights: Copyright © 2012 KISTI All Rights Reserved.
RMID: 0020119335
DOI: 10.5626/JCSE.2011.5.3.197
Appears in Collections:Computer Science publications

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