Data/hora: 06/06/2024 01:03:15

Biblioteca(s): 	Embrapa Agricultura Digital.	
Data corrente: 	18/04/2005	
Data da última atualização: 	17/01/2020	
Autoria: 	OLIVEIRA, S. R. de M.; ZAÏANE, O. R.	
Afiliação: 	STANLEY ROBSON DE MEDEIROS OLIVEIRA, CNPTIA; OSMAR R. ZAÏANE, University of Alberta.	
Título: 	Achieving privacy preservation when sharing data for clustering.	
Ano de publicação: 	2004	
Fonte/Imprenta: 	In: SECURE DATA MANAGEMENT WORKSHOP, 2004, Toronto. Secure data management: proceedings. Berlin: Springer, 2004. p. 67-82.	
Série: 	(Lectures Notes in Computer Science, 3178).	
ISBN: 	3-540-22983-3	
Idioma: 	Inglês	
Notas: 	SDM 2004. Na publicação: Stanley R.M. Oliveira.	
Conteúdo: 	Abstract. In this paper, we address the problem of protecting the underlying attribute values when sharing data for clustering. The challenge is how to meet privacy requirements and guarantee valid clustering results as well. To achieve this dual goal, we propose a novel spatial data transformation method called Rotation-Based Transformation (RBT). The major features of our data transformation are: a) it is independent of any clustering algorithm, b) it has a sound mathematical foundation; c) it is efficient and accurate; and d) it does not rely on intractability hypotheses from algebra and does not require CPU-intensive operations. We show analytically that although the data are transformed to achieve privacy, we can also get accurate clustering results by the safeguard of the global distances between data points.	
Palavras-Chave: 	Clustering; Clusterização; Privacidade.	
Thesaurus Nal: 	Cluster analysis.	
Categoria do assunto: 	X Pesquisa, Tecnologia e Engenharia	
Registro original: 	Embrapa Agricultura Digital (CNPTIA)	
Exemplares: 	CNPTIA - (10668-1) - UPC - AA - PP -  -  - 2005.00008 - DES
CNPTIA - (10668-2) - UPC - AA - DD -  -  -  - DIS