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Registro Completo |
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Biblioteca(s): |
Embrapa Agricultura Digital. |
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Data corrente: |
07/10/2010 |
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Data da última atualização: |
05/04/2013 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
OLIVEIRA, S. R. de M.; ZAÏANE, O. |
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Afiliação: |
STANLEY ROBSON DE MEDEIROS OLIVEIRA, CNPTIA; OSMAR R. ZAÏANE, University of Alberta. |
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Título: |
Revisiting "privacy preserving clustering by data transformation". |
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Ano de publicação: |
2010 |
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Fonte/Imprenta: |
Journal of Information and Data Management, Belo Horizonte, v. 1, n. 1, p. 53-56, Feb. 2010. |
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Idioma: |
Inglês |
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Conteúdo: |
Preserving 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. |
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Palavras-Chave: |
Clustering; Clusterização; Privacidade em mineração de dados; Recuperação da informação. |
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Thesaurus Nal: |
Information retrieval. |
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Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/23217/1/33-81-2-PB.pdf
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Marc: |
LEADER 01419naa a2200193 a 4500
001 1863828
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008 2010 bl uuuu u00u1 u #d
100 1 $aOLIVEIRA, S. R. de M.
245 $aRevisiting "privacy preserving clustering by data transformation".$h[electronic resource]
260 $c2010
520 $aPreserving 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.
650 $aInformation retrieval
653 $aClustering
653 $aClusterização
653 $aPrivacidade em mineração de dados
653 $aRecuperação da informação
700 1 $aZAÏANE, O.
773 $tJournal of Information and Data Management, Belo Horizonte$gv. 1, n. 1, p. 53-56, Feb. 2010.
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Registro original: |
Embrapa Agricultura Digital (CNPTIA) |
