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The 2020 Census Disclosure Avoidance System TopDown Algorithm

Author

Listed:
  • John M. Abowd
  • Robert Ashmead
  • Ryan Cumings-Menon
  • Simson Garfinkel
  • Micah Heineck
  • Christine Heiss
  • Robert Johns
  • Daniel Kifer
  • Philip Leclerc
  • Ashwin Machanavajjhala
  • Brett Moran
  • William Sexton
  • Matthew Spence
  • Pavel Zhuravlev

Abstract

The Census TopDown Algorithm (TDA) is a disclosure avoidance system using differential privacy for privacy-loss accounting. The algorithm ingests the final, edited version of the 2020 Census data and the final tabulation geographic definitions. The algorithm then creates noisy versions of key queries on the data, referred to as measurements, using zero-Concentrated Differential Privacy. Another key aspect of the TDA are invariants, statistics that the Census Bureau has determined, as matter of policy, to exclude from the privacy-loss accounting. The TDA post-processes the measurements together with the invariants to produce a Microdata Detail File (MDF) that contains one record for each person and one record for each housing unit enumerated in the 2020 Census. The MDF is passed to the 2020 Census tabulation system to produce the 2020 Census Redistricting Data (P.L. 94-171) Summary File. This paper describes the mathematics and testing of the TDA for this purpose.

Suggested Citation

  • John M. Abowd & Robert Ashmead & Ryan Cumings-Menon & Simson Garfinkel & Micah Heineck & Christine Heiss & Robert Johns & Daniel Kifer & Philip Leclerc & Ashwin Machanavajjhala & Brett Moran & William, 2022. "The 2020 Census Disclosure Avoidance System TopDown Algorithm," Papers 2204.08986, arXiv.org.
    • Handle: RePEc:arx:papers:2204.08986
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    References listed on IDEAS

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    1. John M. Abowd & Ian M. Schmutte, 2015. "Economic Analysis and Statistical Disclosure Limitation," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 50(1 (Spring), pages 221-293.
    2. John M. Abowd & Ian M. Schmutte, 2019. "An Economic Analysis of Privacy Protection and Statistical Accuracy as Social Choices," American Economic Review, American Economic Association, vol. 109(1), pages 171-202, January.
    3. John M. Abowd & Ian M. Schmutte, 2015. "Economic Analysis and Statistical Disclosure Limitation," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 46(1 (Spring), pages 221-293.
    4. 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.
    5. Wasserman, Larry & Zhou, Shuheng, 2010. "A Statistical Framework for Differential Privacy," Journal of the American Statistical Association, American Statistical Association, vol. 105(489), pages 375-389.
    6. Howard Hogan & Patrick Cantwell & Jason Devine & Vincent Mule & Victoria Velkoff, 2013. "Quality and the 2010 Census," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 32(5), pages 637-662, October.
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    Cited by:

    1. Yue Lin & Ningchuan Xiao, 2023. "Assessing the Impact of Differential Privacy on Population Uniques in Geographically Aggregated Data: The Case of the 2020 U.S. Census," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 42(5), pages 1-20, October.

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