Articles producció científica> Enginyeria Informàtica i Matemàtiques

Database Reconstruction Is Not So Easy and Is Different from Reidentification

  • Datos identificativos

    Identificador: imarina:9385381
    Autores:
    Muralidhar, KrishnamurtyDomingo-Ferrer, Josep
    Resumen:
    In recent years, it has been claimed that releasing accurate statistical information on a database is likely to allow its complete reconstruction. Differential privacy has been suggested as the appropriate methodology to prevent these attacks. These claims have recently been taken very seriously by the U.S. Census Bureau and led them to adopt differential privacy for releasing U.S. Census data. This in turn has caused consternation among users of the Census data due to the lack of accuracy of the protected outputs. It has also brought legal action against the U.S. Department of Commerce. In this article, we trace the origins of the claim that releasing information on a database automatically makes it vulnerable to being exposed by reconstruction attacks and we show that this claim is, in fact, incorrect. We also show that reconstruction can be averted by properly using traditional statistical disclosure control (SDC) techniques. We further show that the geographic level at which exact counts are released is even more relevant to protection than the actual SDC method employed. Finally, we caution against confusing reconstruction and reidentification: using the quality of reconstruction as a metric of reidentification results in exaggerated reidentification risk figures.
  • Otros:

    Autor según el artículo: Muralidhar, Krishnamurty; Domingo-Ferrer, Josep
    Departamento: Enginyeria Informàtica i Matemàtiques
    Autor/es de la URV: Domingo Ferrer, Josep
    Palabras clave: Database privacy Database reconstruction Differential privac Differential privacy Privac Security Statistical disclosure control
    Resumen: In recent years, it has been claimed that releasing accurate statistical information on a database is likely to allow its complete reconstruction. Differential privacy has been suggested as the appropriate methodology to prevent these attacks. These claims have recently been taken very seriously by the U.S. Census Bureau and led them to adopt differential privacy for releasing U.S. Census data. This in turn has caused consternation among users of the Census data due to the lack of accuracy of the protected outputs. It has also brought legal action against the U.S. Department of Commerce. In this article, we trace the origins of the claim that releasing information on a database automatically makes it vulnerable to being exposed by reconstruction attacks and we show that this claim is, in fact, incorrect. We also show that reconstruction can be averted by properly using traditional statistical disclosure control (SDC) techniques. We further show that the geographic level at which exact counts are released is even more relevant to protection than the actual SDC method employed. Finally, we caution against confusing reconstruction and reidentification: using the quality of reconstruction as a metric of reidentification results in exaggerated reidentification risk figures.
    Áreas temáticas: Ciencias sociales Economia Educação General o multidisciplinar Matemática / probabilidade e estatística Social sciences, mathematical methods Social statistics and informatics Statistics & probability Statistics and probability
    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: 2024-10-12
    Versión del articulo depositado: info:eu-repo/semantics/publishedVersion
    Enlace a la fuente original: https://journals.sagepub.com/doi/abs/10.2478/jos-2023-0017
    Referencia al articulo segun fuente origial: Journal Of Official Statistics. 39 (3): 381-398
    Referencia de l'ítem segons les normes APA: Muralidhar, Krishnamurty; Domingo-Ferrer, Josep (2023). Database Reconstruction Is Not So Easy and Is Different from Reidentification. Journal Of Official Statistics, 39(3), 381-398. DOI: 10.2478/jos-2023-0017
    URL Documento de licencia: https://repositori.urv.cat/ca/proteccio-de-dades/
    DOI del artículo: 10.2478/jos-2023-0017
    Entidad: Universitat Rovira i Virgili
    Año de publicación de la revista: 2023
    Tipo de publicación: Journal Publications
  • Palabras clave:

    Social Sciences, Mathematical Methods,Statistics & Probability,Statistics and Probability
    Database privacy
    Database reconstruction
    Differential privac
    Differential privacy
    Privac
    Security
    Statistical disclosure control
    Ciencias sociales
    Economia
    Educação
    General o multidisciplinar
    Matemática / probabilidade e estatística
    Social sciences, mathematical methods
    Social statistics and informatics
    Statistics & probability
    Statistics and probability
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