Use este identificador para citar ou linkar para este item: http://www.alice.cnptia.embrapa.br/alice/handle/doc/863828
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dc.contributor.authorOLIVEIRA, S. R. de M.pt_BR
dc.contributor.authorZAÏANE, O.pt_BR
dc.date.accessioned2011-04-10T11:11:11Zpt_BR
dc.date.available2011-04-10T11:11:11Zpt_BR
dc.date.created2010-10-07pt_BR
dc.date.issued2010pt_BR
dc.identifier.citationJournal of Information and Data Management, Belo Horizonte, v. 1, n. 1, p. 53-56, Feb. 2010.pt_BR
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/863828pt_BR
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.pt_BR
dc.language.isoengeng
dc.rightsopenAccesseng
dc.subjectClusterizaçãopt_BR
dc.subjectPrivacidade em mineração de dadospt_BR
dc.subjectRecuperação da informaçãopt_BR
dc.subjectClusteringpt_BR
dc.titleRevisiting "privacy preserving clustering by data transformation".pt_BR
dc.typeArtigo de periódicopt_BR
dc.date.updated2013-04-05T11:11:11Zpt_BR
dc.subject.nalthesaurusInformation retrievalpt_BR
riaa.ainfo.id863828pt_BR
riaa.ainfo.lastupdate2013-04-05pt_BR
dc.contributor.institutionSTANLEY ROBSON DE MEDEIROS OLIVEIRA, CNPTIA; OSMAR R. ZAÏANE, University of Alberta.pt_BR
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