Data/hora: 06/06/2024 09:03:09

Biblioteca(s): | Embrapa Agricultura Digital.
Data corrente: | 07/03/2007
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: | A unified framework for protecting sensitive association rules in business collaboration.
Ano de publicação: | 2006
Fonte/Imprenta: | International Journal Business Intelligence and Data Mining, v. 1, n. 3, p. 247-287, 2006.
DOI: | https://doi.org/10.1504/IJBIDM.2006.009135
Idioma: | Inglês
Notas: | Na publicação: Stanley R. M. Oliveira.
Conteúdo: | Abstract. The sharing of association rules has been proven beneficial in business collaboration, but requires privacy safeguards. One may decide to disclose only part of the knowledge and conceal strategic patterns called sensitive rules. The challenge here is how to protect the sensitive rules without losing the benefit of mining. To address this problem, we propose a unified framework that combines: a set of algorithms to protect sensitive knowledge; retrieval facilities to speed up the process of knowledge protecting; and a set of metrics to evaluate the effectiveness of the proposed algorithms in terms of information loss and private information disclosure
Palavras-Chave: | Association rules; Business collaboration; Collaborative projects; Competitive knowledge; conhecimento competitivo; conhecimento sensitivo; Information loss; Knowledge protection; Mineração de dados; Privacy preserving data mining; Privacy safeguards; Private information disclosure; Proteção do conhecimento; Regras de associação; Regras sensitivas; Sensitive knowledge; Sensitive rules.
Categoria do assunto: | X Pesquisa, Tecnologia e Engenharia
Registro original: | Embrapa Agricultura Digital (CNPTIA)

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CNPTIA - (11420-1) - UPC - AP - -- -  -  - 2007.00008 - DES
CNPTIA - (11420-2) - UPC - AP - DD -  -  -  - DIS