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| 2. |  | AMARAL, T. B.; CORRÊA, E. S.; COSTA, F. P. Avaliação econômica de diferentes tecnologias adotadas na reprodução de bovinos de corte. In: AMARAL, T. B.; SERENO, J. R. B.; PELLEGRIN, A. O. (Ed.). Fertilidade, funcionalidade e genética de touros zebuínos. Corumbá: Embrapa Pantanal; Campo Grande, MS: Embrapa Gado de Corte; Planaltina, DF: Embrapa Cerrados, 2009. p. 195-216| Biblioteca(s): Embrapa Gado de Corte. |
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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: |
21/03/2005 |
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Data da última atualização: |
25/10/2012 |
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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: |
Data transformation for privacy-preserving data mining. |
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Ano de publicação: |
2004 |
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Fonte/Imprenta: |
2004. |
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Páginas: |
167 p. |
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Idioma: |
Inglês |
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Notas: |
Thesis (Doctor of Philosophy) - University of Alberta, Edmonton, Canadá. |
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Conteúdo: |
The sharing of data is often beneficial in data mining applications. It has been proven useful to support both decision-making processes and to promote social goals. However, the sharing of data has also raised a number of ethical issues. Some such issues include those of privacy, data security, and intellectual property rights.
In this thesis, we focus primarily on privacy issues in data mining, notably when data are shared before mining. Specifically, we consider some scenarios in which applications of association rule mining and data clustering require privacy safeguards. Addressing privacy preservation in such scenarios is complex. One must not only meet privacy requirements but also guarantee valid data rnining results. This status indicates the pressing need for rethinking mechanisnis to enforce privacy safeguards without losing the benefit of mining. These mechanisms can lead to new privacy control methods to convert a database into a new one in such a waY as to preserve the main features of the original database for mining. In particular, we address the problem of transforming a database to be shared into a new one that conceals private information while preserving the general patterrns and trends from the original database. To address this challening problem, we propose a unified framework for privacy-preserving data mining that ensures that the mining process will not violate privacy up to a certain degree of security. The frarnework encompasses a family of privacy-preserving data transformation rnethods, a library of algoritImis, retrieval facilities to speed up the transformation process, and a set of metrics to evaluate the effectiveness of the proposed algorithms, in terms of information loss, and to quantify how much private information has been disclosed. Our investigation concludes that privacy-preserving data mining is to some extent possible. We demonstrate empirically and tlleoretically the practicality and feasibility of achieving privacy preservation in data mining. Our experiments reveal that our framework is efféctive, meets privacy requírements. and guarantees valid data mining results while protecting sensitive information (e.g., sensitive knowIedge and individuals' privacy). MenosThe sharing of data is often beneficial in data mining applications. It has been proven useful to support both decision-making processes and to promote social goals. However, the sharing of data has also raised a number of ethical issues. Some such issues include those of privacy, data security, and intellectual property rights.
In this thesis, we focus primarily on privacy issues in data mining, notably when data are shared before mining. Specifically, we consider some scenarios in which applications of association rule mining and data clustering require privacy safeguards. Addressing privacy preservation in such scenarios is complex. One must not only meet privacy requirements but also guarantee valid data rnining results. This status indicates the pressing need for rethinking mechanisnis to enforce privacy safeguards without losing the benefit of mining. These mechanisms can lead to new privacy control methods to convert a database into a new one in such a waY as to preserve the main features of the original database for mining. In particular, we address the problem of transforming a database to be shared into a new one that conceals private information while preserving the general patterrns and trends from the original database. To address this challening problem, we propose a unified framework for privacy-preserving data mining that ensures that the mining process will not violate privacy up to a certain degree of security. The frarnework encompasses a family of privacy... Mostrar Tudo |
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Palavras-Chave: |
Data mining; Mineração de dados; Preservação de dados; Privacidade; Transformação de dados. |
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Thesagro: |
Tecnologia da Informação. |
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Thesaurus NAL: |
Information technology. |
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Categoria do assunto: |
--
X Pesquisa, Tecnologia e Engenharia |
|
Marc: |
LEADER 02888nam a2200217 a 4500
001 1009322
005 2012-10-25
008 2004 bl uuuu m 00u1 u #d
100 1 $aOLIVEIRA, S. R. de M.
245 $aData transformation for privacy-preserving data mining.
260 $a2004.$c2004
300 $a167 p.
500 $aThesis (Doctor of Philosophy) - University of Alberta, Edmonton, Canadá.
520 $aThe sharing of data is often beneficial in data mining applications. It has been proven useful to support both decision-making processes and to promote social goals. However, the sharing of data has also raised a number of ethical issues. Some such issues include those of privacy, data security, and intellectual property rights. In this thesis, we focus primarily on privacy issues in data mining, notably when data are shared before mining. Specifically, we consider some scenarios in which applications of association rule mining and data clustering require privacy safeguards. Addressing privacy preservation in such scenarios is complex. One must not only meet privacy requirements but also guarantee valid data rnining results. This status indicates the pressing need for rethinking mechanisnis to enforce privacy safeguards without losing the benefit of mining. These mechanisms can lead to new privacy control methods to convert a database into a new one in such a waY as to preserve the main features of the original database for mining. In particular, we address the problem of transforming a database to be shared into a new one that conceals private information while preserving the general patterrns and trends from the original database. To address this challening problem, we propose a unified framework for privacy-preserving data mining that ensures that the mining process will not violate privacy up to a certain degree of security. The frarnework encompasses a family of privacy-preserving data transformation rnethods, a library of algoritImis, retrieval facilities to speed up the transformation process, and a set of metrics to evaluate the effectiveness of the proposed algorithms, in terms of information loss, and to quantify how much private information has been disclosed. Our investigation concludes that privacy-preserving data mining is to some extent possible. We demonstrate empirically and tlleoretically the practicality and feasibility of achieving privacy preservation in data mining. Our experiments reveal that our framework is efféctive, meets privacy requírements. and guarantees valid data mining results while protecting sensitive information (e.g., sensitive knowIedge and individuals' privacy).
650 $aInformation technology
650 $aTecnologia da Informação
653 $aData mining
653 $aMineração de dados
653 $aPreservação de dados
653 $aPrivacidade
653 $aTransformação de dados
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