Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/64297
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Type: Conference paper
Title: Anonymizing graphs against weight-based attacks
Author: Li, Y.
Shen, H.
Citation: Proceedings - 10th IEEE International Conference on Data Mining Workshops: ICDMW 2010 / Wei Fan, Wynne Hsu, G. I. Webb, Bing Liu, Chengqi Zhang, D. Gunopulos, Xindong Wu (eds.): pp.491-498
Publisher: IEEE
Publisher Place: USA
Issue Date: 2010
ISBN: 9780769542577
ISSN: 1550-4786
Conference Name: IEEE International Conference on Data Mining Workshops (10th : 2010 : Sydney, NSW)
Statement of
Responsibility: 
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 realworld 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 by having the topological structure and/or basic graph properties as background knowledge. Many previous studies addressing such attack 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. 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: © 2010 IEEE
RMID: 0020105413
DOI: 10.1109/ICDMW.2010.112
Appears in Collections:Computer Science publications

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