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Database Reconstruction Is Not So Easy and Is Different from Reidentification

  • Dades identificatives

    Identificador: imarina:9385381
    Autors:
    Muralidhar, KrishnamurtyDomingo-Ferrer, Josep
    Resum:
    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.
  • Altres:

    Autor segons l'article: Muralidhar, Krishnamurty; Domingo-Ferrer, Josep
    Departament: Enginyeria Informàtica i Matemàtiques
    Autor/s de la URV: Domingo Ferrer, Josep
    Paraules clau: Database privacy Database reconstruction Differential privac Differential privacy Privac Security Statistical disclosure control
    Resum: 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.
    Àrees temàtiques: Ciencias sociales Economia Educação General o multidisciplinar Matemática / probabilidade e estatística Social sciences, mathematical methods Social statistics and informatics Statistics & probability Statistics and probability
    Accès a la llicència d'ús: https://creativecommons.org/licenses/by/3.0/es/
    Adreça de correu electrònic de l'autor: josep.domingo@urv.cat
    Identificador de l'autor: 0000-0001-7213-4962
    Data d'alta del registre: 2024-10-12
    Versió de l'article dipositat: info:eu-repo/semantics/publishedVersion
    Enllaç font original: https://journals.sagepub.com/doi/abs/10.2478/jos-2023-0017
    Referència a l'article segons font original: Journal Of Official Statistics. 39 (3): 381-398
    Referència 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 Document de llicència: https://repositori.urv.cat/ca/proteccio-de-dades/
    DOI de l'article: 10.2478/jos-2023-0017
    Entitat: Universitat Rovira i Virgili
    Any de publicació de la revista: 2023
    Tipus de publicació: Journal Publications
  • Paraules clau:

    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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