Revisiting "privacy preserving clustering by data transformation".

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dc.contributorSTANLEY ROBSON DE MEDEIROS OLIVEIRA, CNPTIA; OSMAR R. ZAÏANE, University of Alberta.
dc.creatorOLIVEIRA, S. R. de M.
dc.creatorZAÏANE, O.
dc.date2011-04-10T11:11:11Z
dc.date2011-04-10T11:11:11Z
dc.date2010-10-07
dc.date2010
dc.date2013-04-05T11:11:11Z
dc.date.accessioned2026-07-07T04:12:25Z
dc.descriptionPreserving the privacy of individuals when data are shared for clustering is a complex problem. The challenge is how to protect the underlying data values subjected to clustering without jeopardizing the similarity between objects under analysis. In this short paper, we revisit a family of geometric data transformation methods (GDTMs) that distort numerical attributes by translations, scalings, rotations, or even by the combination of these geometric transformations. Such a method was designed to address privacy-preserving clustering, in scenarios where data owners must not only meet privacy requirements but also guarantee valid clustering results. We offer a detailed, comprehensive and up-to-date picture of methods for privacy-preserving clustering by data transformation.
dc.identifierJournal of Information and Data Management, Belo Horizonte, v. 1, n. 1, p. 53-56, Feb. 2010.
dc.identifierhttp://www.alice.cnptia.embrapa.br/alice/handle/doc/863828
dc.identifier.urihttp://hdl.handle.net/123456789/454110
dc.languageeng
dc.rightsopenAccess
dc.subjectClusterização
dc.subjectPrivacidade em mineração de dados
dc.subjectRecuperação da informação
dc.subjectClustering
dc.subjectInformation retrieval
dc.titleRevisiting "privacy preserving clustering by data transformation".
dc.typeArtigo de periódico

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