|
|
Registro Completo |
Biblioteca(s): |
Embrapa Agricultura Digital. |
Data corrente: |
07/10/2010 |
Data da última atualização: |
05/04/2013 |
Tipo da produção científica: |
Artigo em Periódico Indexado |
Autoria: |
OLIVEIRA, S. R. de M.; ZAÏANE, O. |
Afiliação: |
STANLEY ROBSON DE MEDEIROS OLIVEIRA, CNPTIA; OSMAR R. ZAÏANE, University of Alberta. |
Título: |
Revisiting "privacy preserving clustering by data transformation". |
Ano de publicação: |
2010 |
Fonte/Imprenta: |
Journal of Information and Data Management, Belo Horizonte, v. 1, n. 1, p. 53-56, Feb. 2010. |
Idioma: |
Inglês |
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. |
Palavras-Chave: |
Clustering; Clusterização; Privacidade em mineração de dados; Recuperação da informação. |
Thesaurus Nal: |
Information retrieval. |
Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/23217/1/33-81-2-PB.pdf
|
Marc: |
LEADER 01419naa a2200193 a 4500 001 1863828 005 2013-04-05 008 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.
Download
Esconder MarcMostrar Marc Completo |
Registro original: |
Embrapa Agricultura Digital (CNPTIA) |
|
Biblioteca |
ID |
Origem |
Tipo/Formato |
Classificação |
Cutter |
Registro |
Volume |
Status |
URL |
Voltar
|
|
Registros recuperados : 1 | |
Registros recuperados : 1 | |
|
Nenhum registro encontrado para a expressão de busca informada. |
|
|