Articles producció científica> Enginyeria Informàtica i Matemàtiques

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

  • Identification data

    Identifier: imarina:9282651
    Authors:
    Oganian ADomingo-Ferrer J
    Abstract:
    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.
  • Others:

    Author, as appears in the article.: Oganian A; Domingo-Ferrer J
    Department: Enginyeria Informàtica i Matemàtiques
    URV's Author/s: Domingo Ferrer, Josep / OGANIAN, ANNA
    Keywords: 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
    Abstract: 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.
    Thematic Areas: Statistics and probability Software Computer science, theory & methods Ciência da computação
    licence for use: https://creativecommons.org/licenses/by/3.0/es/
    Author's mail: josep.domingo@urv.cat
    Author identifier: 0000-0001-7213-4962
    Record's date: 2023-12-16
    Papper version: info:eu-repo/semantics/publishedVersion
    Link to the original source: https://www.tdp.cat/issues16/vol10n01.php
    Papper original source: Transactions On Data Privacy. 10 (1): 61-81
    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
    Licence document URL: https://repositori.urv.cat/ca/proteccio-de-dades/
    Entity: Universitat Rovira i Virgili
    Journal publication year: 2017
    Publication Type: Journal Publications
  • Keywords:

    Computer Science, Theory & Methods,Software,Statistics and Probability
    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
    Statistics and probability
    Software
    Computer science, theory & methods
    Ciência da computação
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