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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. |
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Registro Completo |
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Biblioteca(s): |
Embrapa Agricultura Digital. |
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Data corrente: |
19/08/2004 |
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Data da última atualização: |
17/01/2020 |
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Autoria: |
OLIVEIRA, S. R. de M.; ZAÏANE, O. R.; SAYGIN, Y. |
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Afiliação: |
STANLEY ROBSON DE MEDEIROS OLIVEIRA, CNPTIA; OSMAR R. ZAÏANE, University of Alberta; YÜCEL SAYGIN, Sabanci University. |
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Título: |
Secure association rule sharing. |
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Ano de publicação: |
2004 |
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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. |
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Páginas: |
p. 74-85. |
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Série: |
(Lecture notes in artificial intelligence, 3056). |
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DOI: |
https://doi.org/10.1007/978-3-540-24775-3_10 |
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Idioma: |
Inglês |
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Notas: |
Editores: Honghua Dai, Ramakrishnan Srikant, Chengqi Zhang. PAKDD 2004. Na publicação: Stanley R. M. Oliveira. |
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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. |
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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. |
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Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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Marc: |
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024 7 $ahttps://doi.org/10.1007/978-3-540-24775-3_10$2DOI
100 1 $aOLIVEIRA, S. R. de M.
245 $aSecure association rule sharing.$h[electronic resource]
260 $aIn: PACIFIC-ASIA CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, 8., 2004, Sidney, Australia. Advances in knowledge discovery and data mining: proceedings. Berlin: Springer$c2004
300 $ap. 74-85.
490 $a(Lecture notes in artificial intelligence, 3056).
500 $aEditores: Honghua Dai, Ramakrishnan Srikant, Chengqi Zhang. PAKDD 2004. Na publicação: Stanley R. M. Oliveira.
520 $aThe 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.
653 $aData mining
653 $aData sanitization
653 $aMineração de dados
653 $aPreservação de privacidade
653 $aPrivacy preserving data mining
653 $aProtecting sensitive knowledge
653 $aRegras de associação
653 $aSanitizing algorithms
653 $aSharing association rules
700 1 $aZAÏANE, O. R.
700 1 $aSAYGIN, Y.
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| 15. |  | 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 : 18 | |
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