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An in-depth examination of requirements for disclosure risk assessment

Author

Listed:
  • Ron S. Jarmin

    (a U.S. Census Bureau, Office of the Deputy Director , Washington , DC 20233)

  • John M. Abowd

    (b Department of Economics , Cornell University , Ithaca , NY 14853)

  • Robert Ashmead

    (a U.S. Census Bureau, Office of the Deputy Director , Washington , DC 20233)

  • Ryan Cumings-Menon

    (a U.S. Census Bureau, Office of the Deputy Director , Washington , DC 20233)

  • Nathan Goldschlag

    (a U.S. Census Bureau, Office of the Deputy Director , Washington , DC 20233)

  • Michael B. Hawes

    (a U.S. Census Bureau, Office of the Deputy Director , Washington , DC 20233)

  • Sallie Ann Keller

    (c Biocomplexity Institute , University of Virginia , Charlottesville , VA 22904)

  • Daniel Kifer

    (d Department of Computer Science and Engineering , Penn State University , University Park , PA 16802)

  • Philip Leclerc

    (a U.S. Census Bureau, Office of the Deputy Director , Washington , DC 20233)

  • Jerome P. Reiter

    (e Department of Statistical Science , Duke University , Durham , NC 27708)

  • Rolando A. Rodríguez

    (a U.S. Census Bureau, Office of the Deputy Director , Washington , DC 20233)

  • Ian Schmutte

    (f Department of Economics , University of Georgia , Athens , GA 30602)

  • Victoria A. Velkoff

    (a U.S. Census Bureau, Office of the Deputy Director , Washington , DC 20233)

  • Pavel Zhuravlev

    (a U.S. Census Bureau, Office of the Deputy Director , Washington , DC 20233)

Abstract

The use of formal privacy to protect the confidentiality of responses in the 2020 Decennial Census of Population and Housing has triggered renewed interest and debate over how to measure the disclosure risks and societal benefits of the published data products. We argue that any proposal for quantifying disclosure risk should be based on prespecified, objective criteria. We illustrate this approach to evaluate the absolute disclosure risk framework, the counterfactual framework underlying differential privacy, and prior-to-posterior comparisons. We conclude that satisfying all the desiderata is impossible, but counterfactual comparisons satisfy the most while absolute disclosure risk satisfies the fewest. Furthermore, we explain that many of the criticisms levied against differential privacy would be levied against any technology that is not equivalent to direct, unrestricted access to confidential data. More research is needed, but in the near term, the counterfactual approach appears best-suited for privacy versus utility analysis.

Suggested Citation

  • Ron S. Jarmin & John M. Abowd & Robert Ashmead & Ryan Cumings-Menon & Nathan Goldschlag & Michael B. Hawes & Sallie Ann Keller & Daniel Kifer & Philip Leclerc & Jerome P. Reiter & Rolando A. Rodrígue, 2023. "An in-depth examination of requirements for disclosure risk assessment," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 120(43), pages 2220558120-, October.
    • Handle: RePEc:nas:journl:v:120:y:2023:p:e2220558120
    DOI: 10.1073/pnas.2220558120
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