An in-depth examination of requirements for disclosure risk assessment

Ron Jarmin, John Abowd (), Robert Ashmead, Ryan Cumings-Menon, Nathan Goldschlag, Michael B. Hawes, Sallie Ann Keller, Daniel Kifer, Philip Leclerc, Jerome P. Reiter, Rolando A. Rodríguez, Ian Schmutte, Victoria A. Velkoff and Pavel Zhuravlev
Additional contact information
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
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

Proceedings of the National Academy of Sciences, 2023, vol. 120, issue 43, e2220558120

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.

Keywords: federal statistical system; data disclosure risk; data access (search for similar items in EconPapers)
Date: 2023
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https://doi.org/10.1073/pnas.2220558120 (application/pdf)

Related works:
Working Paper: An In-Depth Examination of Requirements for Disclosure Risk Assessment (2023)
Working Paper: An In-Depth Examination of Requirements for Disclosure Risk Assessment (2023)
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Persistent link: https://EconPapers.repec.org/RePEc:nas:journl:v:120:y:2023:p:e2220558120

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