Articles producció científica> Enginyeria Informàtica i Matemàtiques

Database Reconstruction Is Not So Easy and Is Different from Reidentification

  • Identification data

    Identifier: imarina:9385381
    Authors:
    Muralidhar, KrishnamurtyDomingo-Ferrer, Josep
    Abstract:
    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.
  • Others:

    Author, as appears in the article.: Muralidhar, Krishnamurty; Domingo-Ferrer, Josep
    Department: Enginyeria Informàtica i Matemàtiques
    URV's Author/s: Domingo Ferrer, Josep
    Keywords: Database privacy Database reconstruction Differential privac Differential privacy Privac Security Statistical disclosure control
    Abstract: 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.
    Thematic Areas: Ciencias sociales Economia Educação General o multidisciplinar Matemática / probabilidade e estatística Social sciences, mathematical methods Social statistics and informatics Statistics & probability Statistics and probability
    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: 2024-10-12
    Papper version: info:eu-repo/semantics/publishedVersion
    Link to the original source: https://journals.sagepub.com/doi/abs/10.2478/jos-2023-0017
    Papper original source: Journal Of Official Statistics. 39 (3): 381-398
    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
    Licence document URL: https://repositori.urv.cat/ca/proteccio-de-dades/
    Article's DOI: 10.2478/jos-2023-0017
    Entity: Universitat Rovira i Virgili
    Journal publication year: 2023
    Publication Type: Journal Publications
  • Keywords:

    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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