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Registros recuperados : 19 | |
12. | | OLIVEIRA, S. R. de M.; ZAÏANE, O. R.; SAYGIN, Y. Secure association rule sharing. 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. p. 74-85. (Lecture notes in artificial intelligence, 3056). Editores: Honghua Dai, Ramakrishnan Srikant, Chengqi Zhang. PAKDD 2004. Na publicação: Stanley R. M. Oliveira. Biblioteca(s): Embrapa Agricultura Digital. |
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Registros recuperados : 19 | |
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| Acesso ao texto completo restrito à biblioteca da Embrapa Agricultura Digital. Para informações adicionais entre em contato com cnptia.biblioteca@embrapa.br. |
Registro Completo
Biblioteca(s): |
Embrapa Agricultura Digital. |
Data corrente: |
06/11/2003 |
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. ZAÏANE, University of Alberta. |
Título: |
Privacy preserving frequent itemset mining. |
Ano de publicação: |
2002 |
Fonte/Imprenta: |
In: IEEE ICDM WORKSHOP ON PRIVACY, SECURITY AND DATA MINING, 2002, Maebashi. Proceedings... Sydney: Australian Computer Society, 2002. p. 43-54. |
Idioma: |
Inglês |
Notas: |
Editors: Chris Clifton, Vladimir Estivill-Castro. Na publicação: Stanley R. M. Oliveira. |
Conteúdo: |
One crucial aspect of privacy preserving frequent itemset mining is the fact that the mining process deals with a trade-off: privacy and accuracy, which are typically contradictory, and improving one usually incurs a cost in the other. One alternative to address this particular problem is to look for a balance between hiding restrictive patterns and disclosing non-restrictive ones. In this paper, we propose a new framework for enforcing privacy in mining frequent itemsets. We combine, in a single framework, techniques for efficiently hiding restrictive patterns: a transaction retrieval engine relying on an inverted file and Boolean queries; and a set of algorithms to "sanitize" a database. In addition, we introduce performance measures for mining frequent itemsets that quantify the fraction of mining patterns which are preserved after sanitizing a database. We also report the results of a performance evaluation of our research prototype and an analysis of the results. |
Palavras-Chave: |
Assosiation rule mining; Data mining; Frequent itemset mining; Mineração de dados; Preservação de privacidade; Privacy preservation in association rule mining; Privacy preserving data mining; Regras de associação; Security; Segurança. |
Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
Marc: |
LEADER 01939nam a2200253 a 4500 001 1008566 005 2020-01-17 008 2002 bl uuuu u00u1 u #d 100 1 $aOLIVEIRA, S. R. de M. 245 $aPrivacy preserving frequent itemset mining.$h[electronic resource] 260 $aIn: IEEE ICDM WORKSHOP ON PRIVACY, SECURITY AND DATA MINING, 2002, Maebashi. Proceedings... Sydney: Australian Computer Society, 2002. p. 43-54.$c2002 500 $aEditors: Chris Clifton, Vladimir Estivill-Castro. Na publicação: Stanley R. M. Oliveira. 520 $aOne crucial aspect of privacy preserving frequent itemset mining is the fact that the mining process deals with a trade-off: privacy and accuracy, which are typically contradictory, and improving one usually incurs a cost in the other. One alternative to address this particular problem is to look for a balance between hiding restrictive patterns and disclosing non-restrictive ones. In this paper, we propose a new framework for enforcing privacy in mining frequent itemsets. We combine, in a single framework, techniques for efficiently hiding restrictive patterns: a transaction retrieval engine relying on an inverted file and Boolean queries; and a set of algorithms to "sanitize" a database. In addition, we introduce performance measures for mining frequent itemsets that quantify the fraction of mining patterns which are preserved after sanitizing a database. We also report the results of a performance evaluation of our research prototype and an analysis of the results. 653 $aAssosiation rule mining 653 $aData mining 653 $aFrequent itemset mining 653 $aMineração de dados 653 $aPreservação de privacidade 653 $aPrivacy preservation in association rule mining 653 $aPrivacy preserving data mining 653 $aRegras de associação 653 $aSecurity 653 $aSegurança 700 1 $aZAÏANE, O. R.
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Embrapa Agricultura Digital (CNPTIA) |
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