Nothing Special   »   [go: up one dir, main page]
IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2204.08986.html
   My bibliography  Save this paper

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
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2204.08986
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Raj Chetty & John N. Friedman, 2019. "A Practical Method to Reduce Privacy Loss When Disclosing Statistics Based on Small Samples," AEA Papers and Proceedings, American Economic Association, vol. 109, pages 414-420, May.
    2. Braathen, Christian & Thorsen, Inge & Ubøe, Jan, 2022. "Adjusting for Cell Suppression in Commuting Trip Data," Discussion Papers 2022/13, Norwegian School of Economics, Department of Business and Management Science.
    3. Michler, Jeffrey D. & Josephson, Anna & Kilic, Talip & Murray, Siobhan, 2022. "Privacy protection, measurement error, and the integration of remote sensing and socioeconomic survey data," Journal of Development Economics, Elsevier, vol. 158(C).
    4. John M Abowd & Michael B Hawes, 2022. "Confidentiality Protection in the 2020 US Census of Population and Housing," Papers 2206.03524, arXiv.org, revised Dec 2022.
    5. Vilhuber, Lars, 2023. "Reproducibility and transparency versus privacy and confidentiality: Reflections from a data editor," Journal of Econometrics, Elsevier, vol. 235(2), pages 2285-2294.
    6. Craig Wesley Carpenter & Anders Van Sandt & Scott Loveridge, 2022. "Measurement error in US regional economic data," Journal of Regional Science, Wiley Blackwell, vol. 62(1), pages 57-80, January.
    7. John M. Abowd & Ian M. Schmutte & William Sexton & Lars Vilhuber, 2019. "Suboptimal Provision of Privacy and Statistical Accuracy When They are Public Goods," Papers 1906.09353, arXiv.org.
    8. Raj Chetty & John N. Friedman & Nathaniel Hendren & Maggie R. Jones & Sonya R. Porter, 2018. "The Opportunity Atlas: Mapping the Childhood Roots of Social Mobility," Working Papers 18-42, Center for Economic Studies, U.S. Census Bureau.
    9. 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.
    10. Jahangir Alam M. & Dostie Benoit & Drechsler Jörg & Vilhuber Lars, 2020. "Applying data synthesis for longitudinal business data across three countries," Statistics in Transition New Series, Polish Statistical Association, vol. 21(4), pages 212-236, August.
    11. John M. Abowd & Ian M. Schmutte & William N. Sexton & Lars Vilhuber, 2019. "Why the Economics Profession Must Actively Participate in the Privacy Protection Debate," AEA Papers and Proceedings, American Economic Association, vol. 109, pages 397-402, May.
    12. John M. Abowd & Ian M. Schmutte, 2017. "Revisiting the Economics of Privacy: Population Statistics and Confidentiality Protection as Public Goods," Working Papers 17-37, Center for Economic Studies, U.S. Census Bureau.
    13. William P. O’Hare, 2016. "A State Level Assessment of the Well-Being of Black Children in the United States," Child Indicators Research, Springer;The International Society of Child Indicators (ISCI), vol. 9(1), pages 277-297, March.
    14. Claire McKay Bowen & Fang Liu & Bingyue Su, 2021. "Differentially private data release via statistical election to partition sequentially," METRON, Springer;Sapienza Università di Roma, vol. 79(1), pages 1-31, April.
    15. Chemaya, Nir & Liu, Dingyue, 2024. "The suitability of using Uniswap V2 model to analyze V3 data," Finance Research Letters, Elsevier, vol. 59(C).
    16. Polanec Sašo & Smith Paul A. & Bavdaž Mojca, 2022. "Determination of the Threshold in Cutoff Sampling Using Response Burden with an Application to Intrastat," Journal of Official Statistics, Sciendo, vol. 38(4), pages 1205-1234, December.
    17. Robertas Damasevicius, 2023. "Progress, Evolving Paradigms and Recent Trends in Economic Analysis," Financial Economics Letters, Anser Press, vol. 2(2), pages 35-47, October.
    18. Giuseppe Di Vita, 2023. "The economic impact of legislative complexity and corruption: A cross‐country analysis," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(2), pages 1801-1825, April.
    19. Inbal Dekel & Rachel Cummings & Ori Heffetz & Katrina Ligett, 2024. "Privacy Elasticity: A (Hopefully) Useful New Concept," NBER Chapters, in: Data Privacy Protection and the Conduct of Applied Research: Methods, Approaches and their Consequences, National Bureau of Economic Research, Inc.
    20. Binswanger, Johannes & Oechslin, Manuel, 2020. "Better statistics, better economic policies?," European Economic Review, Elsevier, vol. 130(C).

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:2204.08986. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.