Data/hora: 06/06/2024 09:16:15

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)
Exemplares: | CNPTIA - (15164-1) - UPC - AP - DD -  -  -  - DIS