Data/hora: 06/06/2024 10:59:22

Biblioteca(s): 	Embrapa Agricultura Digital.	
Data corrente: 	07/10/2010	
Data da última atualização: 	05/04/2013	
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: 	http://ainfo.cnptia.embrapa.br/digital/bitstream/item/23217/1/33-81-2-PB.pdf	
Registro original: 	Embrapa Agricultura Digital (CNPTIA)	
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