Local synthesis for disclosure limitation that satisfies probabilistic k-anonymity criterion

Oganian, A.; Domingo-Ferrer, J.

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TÍTULO:

Local synthesis for disclosure limitation that satisfies probabilistic k-anonymity criterion - PC:2893

Handle:

https://hdl.handle.net/20.500.11797/PC2893

Autor/es de la URV:	OGANIAN , ANNA; DOMINGO FERRER, JOSEP
Autor según el artículo:	Oganian, A.; Domingo-Ferrer, J.
Identificador del autor:	; 0000-0001-7213-4962
Año de publicación de la revista:	2017
Tipo de publicación:	Artículo
ISSN:	1888-5063
Enlace a la fuente original:	http://www.tdp.cat/issues16/vol10n01.php
Versión del articulo depositado:	info:eu-repo/semantics/publishedVersion
Acceso a la licencia de uso:	https://creativecommons.org/licenses/by/3.0/es/
Departamento:	Enginyeria Informàtica i Matemàtiques
Grupo de investigación:	Seguretat i Privadesa
URL Documento de licencia:	https://repositori.urv.cat/ca/proteccio-de-dades/
Áreas temáticas:	Matemáticas
Premios i ayudas:	ICREA Acadèmia
Entidad:	Universitat Rovira i Virgili
Fecha de alta del registro:	2017-05-26
Página inicial:	61
Página final:	81
Volumen de revista:	10

Descripción:	Filiació URV: SI DOI: N/D URL: http://www.tdp.cat/issues16/vol10n01.php Before releasing databases which contain sensitive information about individuals, data publishers must apply Statistical Disclosure Limitation (SDL) methods to them, in order to avoid disclosure of sensitive information on any identifiable data subject. SDL methods often consist of masking or synthesizing the original data records in such a way as to minimize the risk of disclosure of the sensitive information while providing data users with accurate information about the population of interest. In this paper we propose a new scheme for disclosure limitation, based on the idea of local synthesis of data. Our approach is predicated on model-based clustering. The proposed method satisfies the requirements of k-anonymity; in particular we use a variant of the k-anonymity privacy model, namely probabilistic k-anonymity, by incorporating constraints on cluster cardinality. Regarding data utility, for continuous attributes, we exactly preserve means and covariances of the original data, while approximately preserving higher-order moments and analyses on subdomains (defined by clusters and cluster combinations). For both continuous and categorical data, our experiments with medical data sets show that, from the point of view of data utility, local synthesis compares very favorably with other methods of disclosure limitation including the sequential regression approach for synthetic data generation.
Cobertura:	Anglès
Tipo:	article Article Artículo Article info:eu-repo/semantics/publishedVersion
Coautor:	Seguretat i Privadesa Enginyeria Informàtica i Matemàtiques Universitat Rovira i Virgili
Títol:	Local synthesis for disclosure limitation that satisfies probabilistic k-anonymity criterion
Materia:	Xifratge (Informàtica) Protecció de dades Probabilistic k-anonymity Mixture model Expectation-Maximization (EM) algorithm Mathematics Matemáticas Matemàtiques 1888-5063
Fecha:	2017
Formato:	311 kb
Autor:	Oganian, A. Domingo-Ferrer, J.
Derechos:	info:eu-repo/semantics/openAccess
Editor:	Publicacions de la Universitat Autònoma de Barcelona

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