Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/64297
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dc.contributor.authorLi, Y.en
dc.contributor.authorShen, H.en
dc.date.issued2010en
dc.identifier.citationProceedings - 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.491-498en
dc.identifier.isbn9780769542577en
dc.identifier.issn1550-4786en
dc.identifier.urihttp://hdl.handle.net/2440/64297-
dc.description.abstractThe 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.en
dc.description.statementofresponsibilityYidong Li and Hong Shenen
dc.language.isoenen
dc.publisherIEEEen
dc.rights© 2010 IEEEen
dc.subjectAnonymity, Weighted Graph, Privacy Preserving Graph Mining, Weight Anonymization.en
dc.titleAnonymizing graphs against weight-based attacksen
dc.typeConference paperen
dc.identifier.rmid0020105413en
dc.contributor.conferenceIEEE International Conference on Data Mining Workshops (10th : 2010 : Sydney, NSW)en
dc.identifier.doi10.1109/ICDMW.2010.112en
dc.publisher.placeUSAen
dc.identifier.pubid31199-
pubs.library.collectionComputer Science publicationsen
pubs.verification-statusVerifieden
pubs.publication-statusPublisheden
dc.identifier.orcidShen, H. [0000-0002-3663-6591]en
Appears in Collections:Computer Science publications

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