Data/hora: 23/09/2024 05:55:59

Biblioteca(s): 	Embrapa Agricultura Digital.	
Data corrente: 	30/10/2003	
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. ZAIANE, University of Alberta.	
Título: 	Foundations for an access control model for privacy preservation in multi-relational association rule mining.	
Ano de publicação: 	2002	
Fonte/Imprenta: 	In: IEEE ICDM WORKSHOP ON PRIVACY, SECURITY AND DATA MINING, 2002, Maebashi, Japan. Proceedings... Australia: Australian Computer Society, 2002. p.19-54	
Idioma: 	Inglês	
Notas: 	Na publicação: Stanley R. M.Oliveira. PSDM 2002.	
Conteúdo: 	Recent data mining algorithms have been designed for application domains that involve several types of objects stored in multiple relations in relational databases. This fact has motivated the increasing number of successful applications of relational data mining over recent years. On the other hand, such applications have introduced a new threat to privacy and information security since from non-sensitive data one is able to infer sensitive information, including personal information, facts or even patterns that are not supposed to be disclosed. The existing access control models adopted to successfully manage the access of information in complex systems present some limitations in the context of data mining tasks. The main reason is that such models were designed to protect the access to explicit data (e.g. tables, attributes, views, etc), whereas data mining tasks deal with the discovery of implicit data (e.g. patterns). In this paper, we take a first step toward an access control model for ensuring privacy in relational data mining, notably in multi-relational association rules (MRAR). In this model, users associated with dfferent mining access levels, even using the same algorithm, are allowed to mine different sets of association rules. We provide the groundwork to build our access control model over existing technologies and discuss some directions for future work.	
Palavras-Chave: 	Access control; Controle de acesso; Data mining; Mineração de dados; Mining access control; Privacidade; Privacy preserving data mining; Segurança.	
Categoria do assunto: 	--	
Registro original: 	Embrapa Agricultura Digital (CNPTIA)	
Exemplares: 	CNPTIA - (9750-1) - UPC - AA - -- -  -  - 2003.00031 - DES
CNPTIA - (9750-2) - UPC - AA - DD -  -  -  - DIS