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 | Acesso ao texto completo restrito à biblioteca da Embrapa Agricultura Digital. Para informações adicionais entre em contato com cnptia.biblioteca@embrapa.br. |
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Registro Completo |
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Biblioteca(s): |
Embrapa Agricultura Digital. |
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Data corrente: |
12/12/2007 |
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Data da última atualização: |
17/01/2020 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
OLIVEIRA, S. R. de M.; ZAÏANE, O. R. |
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Afiliação: |
STANLEY ROBSON DE MEDEIROS OLIVEIRA, CNPTIA; OSMAR R. ZAÏANE, University of Alberta. |
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Título: |
Privacy-preserving clustering to uphold business collaboration: a dimensionality reduction-based transformation approach. |
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Ano de publicação: |
2007 |
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Fonte/Imprenta: |
International Journal of Information Security and Privacy, v. 1, n. 2, p. 13-36, Apr./June, 2007. |
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Idioma: |
Inglês |
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Notas: |
Na publicação: Stanley R. M. Oliveira. |
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Conteúdo: |
While the sharing of data is known to be beneficial in data mining applications and widely acknowledged as advantageous in business, this information sharing can become controversial and thwarted by privacy regulations and other privacy concerns. Data clustering for instance could be more accurate if more information is available, hence the data sharing. Any solution needs to balance the clustering requirements and the privacy issues. Rather than simply hindering data owners from sharing information for data analysis, a solution could be designed to meet privacy requirements and guarantee valid data clustering results. To achieve this dual goal, this article introduces a method for privacy-preserving clustering called dimensionality reduction-based transformation (DRBT). This method relies on the intuition behind random projection to protect the underlying attribute values subjected to cluster analysis. It is shown analytically and empirically that transforming a dataset using DRBT, a data owner can achieve privacy preservation and get accurate clustering with little overhead of communication cost. Such a method presents the following advantages: it is independent of distance-based clustering algorithms, it has a sound mathematical foundation, and it does not require CPU-intensive operations. |
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Palavras-Chave: |
Business collaboration; Clusterização; Data mining; Dimensionality reduction; Mineração de dados; Privacidade; Privacy-preserving clustering; Random projection. |
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Thesaurus Nal: |
Cluster analysis. |
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Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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Marc: |
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001 1003335
005 2020-01-17
008 2007 bl uuuu u00u1 u #d
100 1 $aOLIVEIRA, S. R. de M.
245 $aPrivacy-preserving clustering to uphold business collaboration$ba dimensionality reduction-based transformation approach.$h[electronic resource]
260 $c2007
500 $aNa publicação: Stanley R. M. Oliveira.
520 $aWhile the sharing of data is known to be beneficial in data mining applications and widely acknowledged as advantageous in business, this information sharing can become controversial and thwarted by privacy regulations and other privacy concerns. Data clustering for instance could be more accurate if more information is available, hence the data sharing. Any solution needs to balance the clustering requirements and the privacy issues. Rather than simply hindering data owners from sharing information for data analysis, a solution could be designed to meet privacy requirements and guarantee valid data clustering results. To achieve this dual goal, this article introduces a method for privacy-preserving clustering called dimensionality reduction-based transformation (DRBT). This method relies on the intuition behind random projection to protect the underlying attribute values subjected to cluster analysis. It is shown analytically and empirically that transforming a dataset using DRBT, a data owner can achieve privacy preservation and get accurate clustering with little overhead of communication cost. Such a method presents the following advantages: it is independent of distance-based clustering algorithms, it has a sound mathematical foundation, and it does not require CPU-intensive operations.
650 $aCluster analysis
653 $aBusiness collaboration
653 $aClusterização
653 $aData mining
653 $aDimensionality reduction
653 $aMineração de dados
653 $aPrivacidade
653 $aPrivacy-preserving clustering
653 $aRandom projection
700 1 $aZAÏANE, O. R.
773 $tInternational Journal of Information Security and Privacy$gv. 1, n. 2, p. 13-36, Apr./June, 2007.
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