Título:	Revisiting "privacy preserving clustering by data transformation".
Autor:	OLIVEIRA, S. R. de M. ZAÏANE, O.
Afiliación:	STANLEY ROBSON DE MEDEIROS OLIVEIRA, CNPTIA; OSMAR R. ZAÏANE, University of Alberta.
Año:	2010
Referencia:	Journal of Information and Data Management, Belo Horizonte, v. 1, n. 1, p. 53-56, Feb. 2010.
Descripción:	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.
NAL Thesaurus:	Information retrieval
Palabras clave:	Clusterização Privacidade em mineração de dados Recuperação da informação Clustering
Tipo de Material:	Artigo de periódico
Acceso:	openAccess
Aparece en las colecciones:	Artigo em periódico indexado (CNPTIA)