Autor según el artículo: Oganian A; Domingo-Ferrer J
Departamento: Enginyeria Informàtica i Matemàtiques
Autor/es de la URV: Domingo Ferrer, Josep / OGANIAN, ANNA
Palabras clave: Synthetic data generations Synthetic data Statistical disclosure limitations Statistical disclosure limitation (sdl) Sensitive informations Probabilistic k-anonymity Privacy Population statistics Mixture model Maximum principle K-anonymity Expectation-maximization algorithms Expectation-maximization (em) algorithm Disclosure limitations Data privacy utility synthetic data risk probabilistic k-anonymity mixture model microaggregation expectation-maximization (em) algorithm
Resumen: 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. © 2017, University of Skovde. All rights reserved.
Áreas temáticas: Statistics and probability Software Computer science, theory & methods Ciência da computação
Acceso a la licencia de uso: https://creativecommons.org/licenses/by/3.0/es/
Direcció de correo del autor: josep.domingo@urv.cat
Identificador del autor: 0000-0001-7213-4962
Fecha de alta del registro: 2023-12-16
Versión del articulo depositado: info:eu-repo/semantics/publishedVersion
Enlace a la fuente original: https://www.tdp.cat/issues16/vol10n01.php
URL Documento de licencia: http://repositori.urv.cat/ca/proteccio-de-dades/
Referencia al articulo segun fuente origial: Transactions On Data Privacy. 10 (1): 61-81
Referencia de l'ítem segons les normes APA: Oganian A; Domingo-Ferrer J (2017). Local synthesis for disclosure limitation that satisfies probabilistic k-anonymity criterion. Transactions On Data Privacy, 10(1), 61-81
Entidad: Universitat Rovira i Virgili
Año de publicación de la revista: 2017
Tipo de publicación: Journal Publications
Repositori URV