Multirelational k-anonymity

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dc.citation.epage1421en_US
dc.citation.spage1417en_US
dc.contributor.authorNergiz, M. Ercanen_US
dc.contributor.authorClifton, C.en_US
dc.contributor.authorNergiz, A. Erhanen_US
dc.coverage.spatialIstanbul, Turkey
dc.date.accessioned2016-02-08T11:44:07Zen_US
dc.date.available2016-02-08T11:44:07Zen_US
dc.date.issued2007-04en_US
dc.departmentDepartment of Computer Engineeringen_US
dc.descriptionDate of Conference: 15-20 April 2007
dc.descriptionConference name: IEEE 23rd International Conference on Data Engineering, 2007
dc.description.abstractk-Anonymity protects privacy by ensuring that data cannot be linked to a single individual. In a k-anonymous dataset, any identifying information occurs in at least k tuples. Much research has been done to modify a single table dataset to satisfy anonymity constraints. This paper extends the definitions of k-anonymity to multiple relations and shows that previously proposed methodologies either fail to protect privacy, or overly reduce the utility of the data, in a multiple relation setting. A new clustering algorithm is proposed to achieve multirelational anonymity. © 2007 IEEE.en_US
dc.identifier.doi10.1109/ICDE.2007.369025en_US
dc.identifier.urihttp://hdl.handle.net/11693/27095
dc.language.isoEnglishen_US
dc.publisherIEEE
dc.relation.isversionofhttps://doi.org/10.1109/ICDE.2007.369025en_US
dc.source.titleProceedings - International Conference on Data Engineeringen_US
dc.subjectClustering algorithmsen_US
dc.subjectConstraint theoryen_US
dc.subjectData structuresen_US
dc.subjectDatabase systemsen_US
dc.subjectAnonymity constraintsen_US
dc.subjectMultirelational anonymityen_US
dc.subjectData privacyen_US
dc.titleMultirelational k-anonymityen_US
dc.typeConference Paperen_US
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