Articles producció científica> Enginyeria Informàtica i Matemàtiques

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

  • Datos identificativos

    Identificador: PC:2893
    Autores:
    Oganian, A.Domingo-Ferrer, J.
    Resumen:
    Filiació URV: SI DOI: N/D URL: http://www.tdp.cat/issues16/vol10n01.php
  • Otros:

    Autor según el artículo: Oganian, A.; Domingo-Ferrer, J.
    Departamento: Enginyeria Informàtica i Matemàtiques
    Autor/es de la URV: OGANIAN , ANNA; DOMINGO FERRER, JOSEP
    Palabras clave: Probabilistic k-anonymity Mixture model 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.
    Grupo de investigación: Seguretat i Privadesa
    Áreas temáticas: Mathematics Matemáticas Matemàtiques
    Acceso a la licencia de uso: https://creativecommons.org/licenses/by/3.0/es/
    ISSN: 1888-5063
    Identificador del autor: ; 0000-0001-7213-4962
    Fecha de alta del registro: 2017-05-26
    Página final: 81
    Volumen de revista: 10
    Premios i ayudas: ICREA Acadèmia
    Versión del articulo depositado: info:eu-repo/semantics/publishedVersion
    Enlace a la fuente original: http://www.tdp.cat/issues16/vol10n01.php
    Programa de financiación: european; FP7; DwB plan; ARES; 2010 CSD2007-00004 plan; TIN; TIN2011-27076-C03-01 altres; Grups consolidats; 2009 SGR 1135
    URL Documento de licencia: https://repositori.urv.cat/ca/proteccio-de-dades/
    Entidad: Universitat Rovira i Virgili
    Año de publicación de la revista: 2017
    Página inicial: 61
    Tipo de publicación: Article Artículo Article
  • Palabras clave:

    Xifratge (Informàtica)
    Protecció de dades
    Probabilistic k-anonymity
    Mixture model
    Expectation-Maximization (EM) algorithm
    Mathematics
    Matemáticas
    Matemàtiques
    1888-5063
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