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| 9. |  | 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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 | Acesso ao texto completo restrito à biblioteca da Embrapa Agricultura Digital. Para informações adicionais entre em contato com cnptia.biblioteca@embrapa.br. |
Registro Completo
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Biblioteca(s): |
Embrapa Agricultura Digital. |
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Data corrente: |
30/10/2003 |
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Data da última atualização: |
17/01/2020 |
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Autoria: |
OLIVEIRA, S. R. de M.; ZAÏANE, O. R. |
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Afiliação: |
STANLEY ROBSON DE MEDEIROS OLIVEIRA, CNPTIA; OSMAR R. ZAIANE, University of Alberta. |
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Título: |
Foundations for an acess control model for privacy preservation in multi-relational association rule mining. |
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Ano de publicação: |
2002 |
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Fonte/Imprenta: |
In: IEEE ICDM WORKSHOP ON PRIVACY, SECURITY AND DATA MINING, 2002, Maebashi, Japan. Proceedings... Australia: Australian Computer Society, 2002. p.19-54 |
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Idioma: |
Inglês |
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Notas: |
Na publicação: Stanley R. M.Oliveira. PSDM 2002. |
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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. |
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Palavras-Chave: |
Access control; Controle de acesso; Data mining; Mineração de dados; Mining access control; Privacidade; Privacy preserving data mining; Segurança. |
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Categoria do assunto: |
-- |
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Marc: |
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001 1008542
005 2020-01-17
008 2002 bl uuuu u00u1 u #d
100 1 $aOLIVEIRA, S. R. de M.
245 $aFoundations for an acess control model for privacy preservation in multi-relational association rule mining.$h[electronic resource]
260 $aIn: IEEE ICDM WORKSHOP ON PRIVACY, SECURITY AND DATA MINING, 2002, Maebashi, Japan. Proceedings... Australia: Australian Computer Society, 2002. p.19-54$c2002
500 $aNa publicação: Stanley R. M.Oliveira. PSDM 2002.
520 $aRecent 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.
653 $aAccess control
653 $aControle de acesso
653 $aData mining
653 $aMineração de dados
653 $aMining access control
653 $aPrivacidade
653 $aPrivacy preserving data mining
653 $aSegurança
700 1 $aZAÏANE, O. R.
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Embrapa Agricultura Digital (CNPTIA) |
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