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dc.contributor.authorNergiz, M.E.en_US
dc.contributor.authorClifton, C.en_US
dc.contributor.authorNergiz, A.E.en_US
dc.date.accessioned2016-02-08T10:03:15Z
dc.date.available2016-02-08T10:03:15Z
dc.date.issued2009en_US
dc.identifier.issn10414347
dc.identifier.urihttp://hdl.handle.net/11693/22677
dc.description.abstractk-Anonymity protects privacy by ensuring that data cannot be linked to a single individual. In a k-anonymous data set, any identifying information occurs in at least k tuples. Much research has been done to modify a single-table data set 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. We also propose two new clustering algorithms to achieve multirelational anonymity. Experiments show the effectiveness of the approach in terms of utility and efficiency. © 2006 IEEE.en_US
dc.language.isoEnglishen_US
dc.source.titleIEEE Transactions on Knowledge and Data Engineeringen_US
dc.relation.isversionofhttp://dx.doi.org/10.1109/TKDE.2008.210en_US
dc.subjectIntegrityen_US
dc.subjectPrivacyen_US
dc.subjectProtectionen_US
dc.subjectRelational databaseen_US
dc.subjectSecurityen_US
dc.subjectIntegrityen_US
dc.subjectPrivacyen_US
dc.subjectProtectionen_US
dc.subjectRelational databaseen_US
dc.subjectSecurityen_US
dc.subjectClustering algorithmsen_US
dc.titleMultirelational k-anonymityen_US
dc.typeArticleen_US
dc.departmentDepartment of Computer Technology and Information Systemsen_US
dc.departmentDepartment of Computer Engineeringen_US
dc.citation.spage1104en_US
dc.citation.epage1117en_US
dc.citation.volumeNumber21en_US
dc.citation.issueNumber8en_US
dc.identifier.doi10.1109/TKDE.2008.210en_US


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