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The Role of Chance in the Census Bureau Database Reconstruction Experiment

Author

Listed:
  • Steven Ruggles

    (University of Minnesota)

  • David Riper

    (University of Minnesota)

Abstract

The Census Bureau plans a new approach to disclosure control for the 2020 census that will add noise to every statistic the agency produces for places below the state level. The Bureau argues the new approach is needed because the confidentiality of census responses is threatened by “database reconstruction,” a technique for inferring individual-level responses from tabular data. The Census Bureau constructed hypothetical individual-level census responses from public 2010 tabular data and matched them to internal census records and to outside sources. The Census Bureau did not compare these results to a null model to demonstrate that their success in matching would not be expected by chance. This is analogous to conducting a clinical trial without a control group. We implement a simple simulation to assess how many matches would be expected by chance. We demonstrate that most matches reported by the Census Bureau experiment would be expected randomly. To extend the metaphor of the clinical trial, the treatment and the placebo produced similar outcomes. The database reconstruction experiment therefore fails to demonstrate a credible threat to confidentiality.

Suggested Citation

  • Steven Ruggles & David Riper, 2022. "The Role of Chance in the Census Bureau Database Reconstruction Experiment," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 41(3), pages 781-788, June.
    • Handle: RePEc:kap:poprpr:v:41:y:2022:i:3:d:10.1007_s11113-021-09674-3
    DOI: 10.1007/s11113-021-09674-3
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    References listed on IDEAS

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    1. Laura McKenna, 2018. "Disclosure Avoidance Techniques Used for the 1970 through 2010 Decennial Censuses of Population and Housing," Working Papers 18-47, Center for Economic Studies, U.S. Census Bureau.
    2. Santos-Lozada, Alexis R & Perez-Rivera, Danilo T & Bhat, Aarti C., 2020. "How differential privacy will affect our understanding of population growth in the United States," SocArXiv pmux7, Center for Open Science.
    3. Alexis R. Santos-Lozada & Jeffrey T. Howard & Ashton M. Verdery, 2020. "How differential privacy will affect our understanding of health disparities in the United States," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 117(24), pages 13405-13412, June.
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    Cited by:

    1. 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.
    2. Claire McKay Bowen & Joshua Snoke & Aaron R. Williams & Andrés F. Barrientos, 2024. "The Case for Researching Applied Privacy Enhancing Technologies," NBER Chapters, in: Data Privacy Protection and the Conduct of Applied Research: Methods, Approaches and their Consequences, National Bureau of Economic Research, Inc.

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