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Registro Completo |
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Biblioteca(s): |
Embrapa Agricultura Digital. |
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Data corrente: |
07/11/2006 |
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Data da última atualização: |
06/09/2022 |
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Tipo da produção científica: |
Boletim de Pesquisa e Desenvolvimento |
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Autoria: |
OLIVEIRA, S. R. de M. |
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Afiliação: |
STANLEY ROBSON DE MEDEIROS OLIVEIRA, CNPTIA. |
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Título: |
Heuristics for protecting competitive knowledge in association rule mining. |
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Ano de publicação: |
2006 |
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Fonte/Imprenta: |
Campinas: Embrapa Informática Agropecuária, 2006. |
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Páginas: |
48 p. |
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Série: |
(Embrapa Informática Agropecuária. Boletim de pesquisa e desenvolvimento, 13). |
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ISSN: |
1677-9266 |
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Idioma: |
Inglês |
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Conteúdo: |
The sharing of data for mining has been proven beneficial in industry, but requires privacy safeguards. Some companies prefer to share their data for collaboration, while others decide to share only the patterns discovered from their data. The goal of these companies is to disclose only part of the knowledge and conceal a group of sensitive rules (competitive knowledge) that are paramount for strategic decisions. These rules must be protected before sharing and need to remain private. The challenge here is how to protect the sensitive rules without putting at risk the effectiveness of data mining per se. This work presents a framework for protecting sensitive knowledge in Association Rule Mining. The framework is composed of a set of heuristics and metrics to evaluate the effectiveness of these heuristics in terms of information loss and knowledge protection. |
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Palavras-Chave: |
Competitive knowledge; Conhecimento competitivo; Heurística; Knowledge protection; Preservação de privacidade em mineração de dados; Preservação de privacidade em mineração de regras de associação; Privacy-preserving association rule mining; Privacy-preserving data mining; Proteção de conhecimento; Regras sensíveis; Sensitive knowledge; Sensitive rules. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/CNPTIA/11297/1/bp13.pdf
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Marc: |
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001 1004885
005 2022-09-06
008 2006 bl uuuu u0uu1 u #d
022 $a1677-9266
100 1 $aOLIVEIRA, S. R. de M.
245 $aHeuristics for protecting competitive knowledge in association rule mining.$h[electronic resource]
260 $aCampinas: Embrapa Informática Agropecuária$c2006
300 $a48 p.
490 $a(Embrapa Informática Agropecuária. Boletim de pesquisa e desenvolvimento, 13).
520 $aThe sharing of data for mining has been proven beneficial in industry, but requires privacy safeguards. Some companies prefer to share their data for collaboration, while others decide to share only the patterns discovered from their data. The goal of these companies is to disclose only part of the knowledge and conceal a group of sensitive rules (competitive knowledge) that are paramount for strategic decisions. These rules must be protected before sharing and need to remain private. The challenge here is how to protect the sensitive rules without putting at risk the effectiveness of data mining per se. This work presents a framework for protecting sensitive knowledge in Association Rule Mining. The framework is composed of a set of heuristics and metrics to evaluate the effectiveness of these heuristics in terms of information loss and knowledge protection.
653 $aCompetitive knowledge
653 $aConhecimento competitivo
653 $aHeurística
653 $aKnowledge protection
653 $aPreservação de privacidade em mineração de dados
653 $aPreservação de privacidade em mineração de regras de associação
653 $aPrivacy-preserving association rule mining
653 $aPrivacy-preserving data mining
653 $aProteção de conhecimento
653 $aRegras sensíveis
653 $aSensitive knowledge
653 $aSensitive rules
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Registro original: |
Embrapa Agricultura Digital (CNPTIA) |
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| Registros recuperados : 260 | |
| 7. |  | OLIVEIRA, S. R. de M. Privacy-preserving data mining. In: WANG, J. Encyclopedia of data warehousing and mining. 2nd ed. Hershey: Information Science Reference, 2009. v. III p. 1582-1588. Na publicação: Stanley R. M. Oliveira.| Tipo: Capítulo em Livro Técnico-Científico |
| Biblioteca(s): Embrapa Agricultura Digital. |
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| Registros recuperados : 260 | |
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