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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: |
04/12/2008 |
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Data da última atualização: |
16/01/2020 |
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Tipo da produção científica: |
Capítulo em Livro Técnico-Científico |
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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 RACHID ZAÏANE, University of Alberta. |
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Título: |
Business collaboration by privacy-preserving clustering. |
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Ano de publicação: |
2008 |
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Fonte/Imprenta: |
In: EYOB, E. Social implications of data mining and information privacy: interdisciplinary frameworks and solucitions. Hershey: Information Science Reference, 2008. chap. 7, p. 113-133. |
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ISBN: |
978-1-60566-197-1 |
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Idioma: |
Inglês |
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Notas: |
Na publicação: Stanley R. M. Oliveira. |
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Conteúdo: |
The sharing of data is beneficial in data mining applications and widely acknowledged as advantageous in business. However, information sharing can become controversial and thwarted by privacy regulations and other privacy concerns. 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 chapter 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. The advantages of such a method are: 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: |
Data mining; DRBT; Mineração de dados; Privacidade. |
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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 1008033
005 2020-01-16
008 2008 bl uuuu u00u1 u #d
020 $a978-1-60566-197-1
100 1 $aOLIVEIRA, S. R. de M.
245 $aBusiness collaboration by privacy-preserving clustering.$h[electronic resource]
260 $c2008
500 $aNa publicação: Stanley R. M. Oliveira.
520 $aThe sharing of data is beneficial in data mining applications and widely acknowledged as advantageous in business. However, information sharing can become controversial and thwarted by privacy regulations and other privacy concerns. 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 chapter 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. The advantages of such a method are: 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 $aData mining
653 $aDRBT
653 $aMineração de dados
653 $aPrivacidade
700 1 $aZAÏANE, O. R.
773 $tIn: EYOB, E. Social implications of data mining and information privacy: interdisciplinary frameworks and solucitions. Hershey: Information Science Reference, 2008. chap. 7, p. 113-133.
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