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 | Acesso ao texto completo restrito à biblioteca da Embrapa Semiárido. Para informações adicionais entre em contato com cpatsa.biblioteca@embrapa.br. |
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Registro Completo |
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Biblioteca(s): |
Embrapa Semiárido. |
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Data corrente: |
23/06/2009 |
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Data da última atualização: |
05/03/2024 |
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Autoria: |
BONNAL, P. |
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Afiliação: |
PHILIPPE BONNAL, CIRAD. |
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Título: |
Appui aux projets du programmpe P.09 de l' Embrapa. |
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Ano de publicação: |
1996 |
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Fonte/Imprenta: |
Montpellier: CIRAD-SAR, 1996. |
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Páginas: |
22 p. |
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Série: |
(CIRAD-SAR. 2). |
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Idioma: |
Francês |
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Conteúdo: |
Ce séjour au Brésil avait pour but d'appuyer des opérations de Recherche-Développement conduites par l'EMBRAPA dans le cadre de notre coopération avec le Prograrnrne National sur l' Agriculture Familiale. Durant Ia mission, outre Ia participation au séminaire du Programme à Petrolina, trois projets ont été visités et diverses prestations ont été réalisées: formation d'agents du développement dans l'Etat de Rio Grande do Sul (EMATERlRS) aux techniques de diagnostic rapide en exploitations agricoles; appui au traitement des données dans l'Etat du Pernambuco (EMBRAP AlCPA TSA); suivi du projet silvânia et appui à Ia programmation des activités de Recherche-Développement (EMBRAPA AlCPAC). |
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Palavras-Chave: |
CIRAD; Petrolina; Programa 09; Programa Nacional de Agricultura Familiar. |
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Thesagro: |
Agricultura familiar; Pesquisa agrícola; Transferência de tecnologia. |
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Thesaurus Nal: |
Agricultural research; Family farms; Technology transfer. |
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Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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Marc: |
LEADER 01400nam a2200253 a 4500
001 1130525
005 2024-03-05
008 1996 bl uuuu u0uu1 u #d
100 1 $aBONNAL, P.
245 $aAppui aux projets du programmpe P.09 de l' Embrapa.
260 $aMontpellier: CIRAD-SAR$c1996
300 $a22 p.
490 $a(CIRAD-SAR. 2).
520 $aCe séjour au Brésil avait pour but d'appuyer des opérations de Recherche-Développement conduites par l'EMBRAPA dans le cadre de notre coopération avec le Prograrnrne National sur l' Agriculture Familiale. Durant Ia mission, outre Ia participation au séminaire du Programme à Petrolina, trois projets ont été visités et diverses prestations ont été réalisées: formation d'agents du développement dans l'Etat de Rio Grande do Sul (EMATERlRS) aux techniques de diagnostic rapide en exploitations agricoles; appui au traitement des données dans l'Etat du Pernambuco (EMBRAP AlCPA TSA); suivi du projet silvânia et appui à Ia programmation des activités de Recherche-Développement (EMBRAPA AlCPAC).
650 $aAgricultural research
650 $aFamily farms
650 $aTechnology transfer
650 $aAgricultura familiar
650 $aPesquisa agrícola
650 $aTransferência de tecnologia
653 $aCIRAD
653 $aPetrolina
653 $aPrograma 09
653 $aPrograma Nacional de Agricultura Familiar
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Embrapa Semiárido (CPATSA) |
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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: |
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: |
LEADER 02014nam a2200289 a 4500
001 1007448
005 2020-01-17
008 2004 bl uuuu u00u1 u #d
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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