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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: |
A privacy-preserving clustering approach toward secure and effective data analysis for business collaboration. |
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Ano de publicação: |
2007 |
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Fonte/Imprenta: |
Computers & Security, v. 26, n. 1, p. 81-93, Feb. 2007. |
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DOI: |
https://doi.org/10.1016/j.cose.2006.08.003 |
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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 has been proven beneficial in data mining applications. However, privacy regulations and other privacy concerns may prevent data owners from sharing information for data analysis. To resolve this challenging problem, data owners must design a solution that meets privacy requirements and guarantees valid data clustering results. To achieve this dual goal, we introduce a new 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. The major features of this method are: (a) it is independent of distance-based clustering algorithms; (b) it has a sound mathematical foundation; and (c) it does not require CPU-intensive operations. We show analytically and empirically that transforming a data set using DRBT, a data owner can achieve privacy preservation and get accurate clustering with a little overhead of communication cost. |
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Palavras-Chave: |
Clusterização; Data mining; Dimensionality reduction; Mineração de dados; Privacy-preserving clustering; Privacy-preserving data mining; Random projection; Segurança. |
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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 1004302
005 2020-01-17
008 2007 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.1016/j.cose.2006.08.003$2DOI
100 1 $aOLIVEIRA, S. R. de M.
245 $aA privacy-preserving clustering approach toward secure and effective data analysis for business collaboration.$h[electronic resource]
260 $c2007
500 $aNa publicação: Stanley R. M. Oliveira.
520 $aThe sharing of data has been proven beneficial in data mining applications. However, privacy regulations and other privacy concerns may prevent data owners from sharing information for data analysis. To resolve this challenging problem, data owners must design a solution that meets privacy requirements and guarantees valid data clustering results. To achieve this dual goal, we introduce a new 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. The major features of this method are: (a) it is independent of distance-based clustering algorithms; (b) it has a sound mathematical foundation; and (c) it does not require CPU-intensive operations. We show analytically and empirically that transforming a data set using DRBT, a data owner can achieve privacy preservation and get accurate clustering with a little overhead of communication cost.
650 $aCluster analysis
653 $aClusterização
653 $aData mining
653 $aDimensionality reduction
653 $aMineração de dados
653 $aPrivacy-preserving clustering
653 $aPrivacy-preserving data mining
653 $aRandom projection
653 $aSegurança
700 1 $aZAÏANE, O. R.
773 $tComputers & Security$gv. 26, n. 1, p. 81-93, Feb. 2007.
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