Data/hora: 25/06/2024 06:08:59

Biblioteca(s): 	Embrapa Agricultura Digital.	
Data corrente: 	19/08/2004	
Data da última atualização: 	17/01/2020	
Autoria: 	OLIVEIRA, S. R. de M.; ZAÏANE, O. R.; SAYGIN, Y.	
Afiliação: 	STANLEY ROBSON DE MEDEIROS OLIVEIRA, CNPTIA; OSMAR R. ZAÏANE, University of Alberta; YÜCEL SAYGIN, Sabanci University.	
Título: 	Secure association rule sharing.	
Ano de publicação: 	2004	
Fonte/Imprenta: 	In: PACIFIC-ASIA CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, 8., 2004, Sidney, Australia. Advances in knowledge discovery and data mining: proceedings. Berlin: Springer, 2004.	
Páginas: 	p. 74-85.	
Série: 	(Lecture notes in artificial intelligence, 3056).	
DOI: 	https://doi.org/10.1007/978-3-540-24775-3_10	
Idioma: 	Inglês	
Notas: 	Editores: Honghua Dai, Ramakrishnan Srikant, Chengqi Zhang. PAKDD 2004. Na publicação: Stanley R. M. Oliveira.	
Conteúdo: 	The sharing of association rules is often beneficial in industry, but requires privacy safeguards. One may decide to disclose only part of the knowledge and conceal strategic patterns which we call restrictive rules. These restrictive rules must be protected before sharing since they are paramount for strategic decisions and need to remain private. To address this challenging problem, we propose a unified framework for protecting sensitive knowledge before sharing. This framework encompasses: (a) an algorithm that sanitizes restrictive rules, while blocking some inference channels. We validate our algorithm against real and synthetic datasets; (b) a set of metrics to evaluate attacks against sensitive knowledge and the impact of the sanitization. We also introduce a taxonomy of sanitizing algorithms and a taxonomy of attacks against sensitive knowledge.	
Palavras-Chave: 	Data mining; Data sanitization; Mineração de dados; Preservação de privacidade; Privacy preserving data mining; Protecting sensitive knowledge; Regras de associação; Sanitizing algorithms; Sharing association rules.	
Categoria do assunto: 	X Pesquisa, Tecnologia e Engenharia	
Registro original: 	Embrapa Agricultura Digital (CNPTIA)	
Exemplares: 	CNPTIA - (10507-1) - UPC - AA - -- -  -  - 2004.00040 - DES
CNPTIA - (10507-2) - UPC - AA - DD -  -  -  - DIS