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Registro Completo |
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Biblioteca(s): |
Embrapa Agricultura Digital. |
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Data corrente: |
18/04/2005 |
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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. ZAÏANE, University of Alberta. |
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Título: |
Achieving privacy preservation when sharing data for clustering. |
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Ano de publicação: |
2004 |
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Fonte/Imprenta: |
In: SECURE DATA MANAGEMENT WORKSHOP, 2004, Toronto. Secure data management: proceedings. Berlin: Springer, 2004. p. 67-82. |
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Série: |
(Lectures Notes in Computer Science, 3178). |
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ISBN: |
3-540-22983-3 |
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Idioma: |
Inglês |
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Notas: |
SDM 2004. Na publicação: Stanley R.M. Oliveira. |
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Conteúdo: |
Abstract. In this paper, we address the problem of protecting the underlying attribute values when sharing data for clustering. The challenge is how to meet privacy requirements and guarantee valid clustering results as well. To achieve this dual goal, we propose a novel spatial data transformation method called Rotation-Based Transformation (RBT). The major features of our data transformation are: a) it is independent of any clustering algorithm, b) it has a sound mathematical foundation; c) it is efficient and accurate; and d) it does not rely on intractability hypotheses from algebra and does not require CPU-intensive operations. We show analytically that although the data are transformed to achieve privacy, we can also get accurate clustering results by the safeguard of the global distances between data points. |
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Palavras-Chave: |
Clustering; Clusterização; Privacidade. |
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Thesaurus Nal: |
Cluster analysis. |
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Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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Marc: |
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001 1009025
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008 2004 bl uuuu u00u1 u #d
020 $a3-540-22983-3
100 1 $aOLIVEIRA, S. R. de M.
245 $aAchieving privacy preservation when sharing data for clustering.$h[electronic resource]
260 $aIn: SECURE DATA MANAGEMENT WORKSHOP, 2004, Toronto. Secure data management: proceedings. Berlin: Springer, 2004. p. 67-82.$c2004
490 $a(Lectures Notes in Computer Science, 3178).
500 $aSDM 2004. Na publicação: Stanley R.M. Oliveira.
520 $aAbstract. In this paper, we address the problem of protecting the underlying attribute values when sharing data for clustering. The challenge is how to meet privacy requirements and guarantee valid clustering results as well. To achieve this dual goal, we propose a novel spatial data transformation method called Rotation-Based Transformation (RBT). The major features of our data transformation are: a) it is independent of any clustering algorithm, b) it has a sound mathematical foundation; c) it is efficient and accurate; and d) it does not rely on intractability hypotheses from algebra and does not require CPU-intensive operations. We show analytically that although the data are transformed to achieve privacy, we can also get accurate clustering results by the safeguard of the global distances between data points.
650 $aCluster analysis
653 $aClustering
653 $aClusterização
653 $aPrivacidade
700 1 $aZAÏANE, O. R.
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Registro original: |
Embrapa Agricultura Digital (CNPTIA) |
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| Registros recuperados : 257 | |
| 3. |  | 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 : 257 | |
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