Data/hora: 17/06/2024 15:57:00

Biblioteca(s): | Embrapa Agricultura Digital.
Data corrente: | 12/12/2007
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: | Privacy-preserving clustering to uphold business collaboration: a dimensionality reduction-based transformation approach.
Ano de publicação: | 2007
Fonte/Imprenta: | International Journal of Information Security and Privacy, v. 1, n. 2, p. 13-36, Apr./June, 2007.
Idioma: | Inglês
Notas: | Na publicação: Stanley R. M. Oliveira.
Conteúdo: | While the sharing of data is known to be beneficial in data mining applications and widely acknowledged as advantageous in business, this information sharing can become controversial and thwarted by privacy regulations and other privacy concerns. Data clustering for instance could be more accurate if more information is available, hence the data sharing. Any solution needs to balance the clustering requirements and the privacy issues. Rather than simply hindering data owners from sharing information for data analysis, a solution could be designed to meet privacy requirements and guarantee valid data clustering results. To achieve this dual goal, this article introduces a method for privacy-preserving clustering called dimensionality reduction-based transformation (DRBT). This method relies on the intuition behind random projection to protect the underlying attribute values subjected to cluster analysis. It is shown analytically and empirically that transforming a dataset using DRBT, a data owner can achieve privacy preservation and get accurate clustering with little overhead of communication cost. Such a method presents the following advantages: it is independent of distance-based clustering algorithms, it has a sound mathematical foundation, and it does not require CPU-intensive operations.
Palavras-Chave: | Business collaboration; Clusterização; Data mining; Dimensionality reduction; Mineração de dados; Privacidade; Privacy-preserving clustering; Random projection.
Thesaurus Nal: | Cluster analysis.
Categoria do assunto: | X Pesquisa, Tecnologia e Engenharia
Registro original: | Embrapa Agricultura Digital (CNPTIA)

Exemplares:

CNPTIA - (11670-1) - UPC - AP - -- -  -  - 2007.00053 - DES
CNPTIA - (11670-2) - UPC - AP - DD -  -  -  - DIS