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General Confidentiality and Utility Metrics for Privacy-Preserving Data Publishing Based on the Permutation Model

  • Datos identificativos

    Identificador: imarina:9052334
    Handle: http://hdl.handle.net/20.500.11797/imarina9052334
  • Autores:

    Josep Domingo-Ferrer
    Krishnamurty Muralidhar
    Maria Bras-Amorós
  • Otros:

    Autor según el artículo: Josep Domingo-Ferrer; Krishnamurty Muralidhar; Maria Bras-Amorós
    Departamento: Enginyeria Informàtica i Matemàtiques
    Autor/es de la URV: Bras Amoros, Maria / Domingo Ferrer, Josep
    Palabras clave: Privacy Correlation-coefficients utility statistical disclosure control sets permutation model paradigm information loss data anonymization confidentiality anonymization anonymity
    Resumen: Anonymization for privacy-preserving data publishing, also known as statistical disclosure control (SDC), can be viewed under the lens of the permutation model. According to this model, any SDC method for individual data records is functionally equivalent to a permutation step plus a noise addition step, where the noise added is marginal, in the sense that it does not alter ranks. Here, we propose metrics to quantify the data confidentiality and utility achieved by SDC methods based on the permutation model. We distinguish two privacy notions: in our work, anonymity refers to subjects and hence mainly to protection against record re-identification, whereas confidentiality refers to the protection afforded to attribute values against attribute disclosure. Thus, our confidentiality metrics are useful even if using a privacy model ensuring an anonymity level ex ante. The utility metric is a general-purpose metric that can be …
    Áreas temáticas: General computer science Engenharias iv Engenharias iii Electrical and electronic engineering Computer science, software engineering Computer science, information systems Computer science, hardware & architecture Computer science (miscellaneous) Computer science (all) 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 maria.bras@urv.cat
    Identificador del autor: 0000-0001-7213-4962 0000-0002-3481-004X
    Fecha de alta del registro: 2023-02-19
    Versión del articulo depositado: info:eu-repo/semantics/acceptedVersion
    Enlace a la fuente original: https://www.computer.org/csdl/journal/tq/5555/01/08966490/1gNEMChWNIk
    URL Documento de licencia: http://repositori.urv.cat/ca/proteccio-de-dades/
    Referencia al articulo segun fuente origial: Ieee Transactions On Dependable And Secure Computing. 18 (5): 2506-2517
    Referencia de l'ítem segons les normes APA: Josep Domingo-Ferrer; Krishnamurty Muralidhar; Maria Bras-Amorós (2021). General Confidentiality and Utility Metrics for Privacy-Preserving Data Publishing Based on the Permutation Model. Ieee Transactions On Dependable And Secure Computing, 18(5), 2506-2517. DOI: 10.1109/TDSC.2020.2968027
    DOI del artículo: 10.1109/TDSC.2020.2968027
    Entidad: Universitat Rovira i Virgili
    Año de publicación de la revista: 2021
    Tipo de publicación: Journal Publications
  • Palabras clave:

    Computer Science (Miscellaneous),Computer Science, Hardware & Architecture,Computer Science, Information Systems,Computer Science, Software Engineering,Electrical and Electronic Engineering
    Privacy
    Correlation-coefficients
    utility
    statistical disclosure control
    sets
    permutation model
    paradigm
    information loss
    data anonymization
    confidentiality
    anonymization
    anonymity
    General computer science
    Engenharias iv
    Engenharias iii
    Electrical and electronic engineering
    Computer science, software engineering
    Computer science, information systems
    Computer science, hardware & architecture
    Computer science (miscellaneous)
    Computer science (all)
    Ciência da computação
  • Documentos:

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