IDEAS home Printed from https://ideas.repec.org/a/nas/journl/v117y2020p13405-13412.html
   My bibliography  Save this article

How differential privacy will affect our understanding of health disparities in the United States

Author

Listed:
  • Alexis R. Santos-Lozada

    (Department of Human Development and Family Studies, The Pennsylvania State University, University Park, PA 16802)

  • Jeffrey T. Howard

    (Department of Public Health, University of Texas at San Antonio, San Antonio, TX 78249)

  • Ashton M. Verdery

    (Department of Sociology and Criminology, The Pennsylvania State University, University Park, PA 16802)

Abstract

The application of a currently proposed differential privacy algorithm to the 2020 United States Census data and additional data products may affect the usefulness of these data, the accuracy of estimates and rates derived from them, and critical knowledge about social phenomena such as health disparities. We test the ramifications of applying differential privacy to released data by studying estimates of US mortality rates for the overall population and three major racial/ethnic groups. We ask how changes in the denominators of these vital rates due to the implementation of differential privacy can lead to biased estimates. We situate where these changes are most likely to matter by disaggregating biases by population size, degree of urbanization, and adjacency to a metropolitan area. Our results suggest that differential privacy will more strongly affect mortality rate estimates for non-Hispanic blacks and Hispanics than estimates for non-Hispanic whites. We also find significant changes in estimated mortality rates for less populous areas, with more pronounced changes when stratified by race/ethnicity. We find larger changes in estimated mortality rates for areas with lower levels of urbanization or adjacency to metropolitan areas, with these changes being greater for non-Hispanic blacks and Hispanics. These findings highlight the consequences of implementing differential privacy, as proposed, for research examining population composition, particularly mortality disparities across racial/ethnic groups and along the urban/rural continuum. Overall, they demonstrate the challenges in using the data products derived from the proposed disclosure avoidance methods, while highlighting critical instances where scientific understandings may be negatively impacted.

Suggested Citation

  • 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.
  • Handle: RePEc:nas:journl:v:117:y:2020:p:13405-13412
    as

    Download full text from publisher

    File URL: http://www.pnas.org/content/117/24/13405.full
    Download Restriction: no
    ---><---

    Citations

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


    Cited by:

    1. J. Tom Mueller & Alexis R. Santos-Lozada, 2022. "The 2020 US Census Differential Privacy Method Introduces Disproportionate Discrepancies for Rural and Non-White Populations," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 41(4), pages 1417-1430, August.
    2. Bi, Xuan & Shen, Xiaotong, 2023. "Distribution-invariant differential privacy," Journal of Econometrics, Elsevier, vol. 235(2), pages 444-453.
    3. Heng Xu & Nan Zhang, 2022. "Implications of Data Anonymization on the Statistical Evidence of Disparity," Management Science, INFORMS, vol. 68(4), pages 2600-2618, April.
    4. Sigurd Dyrting & Abraham Flaxman & Ethan Sharygin, 2022. "Reconstruction of age distributions from differentially private census data," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 41(6), pages 2311-2329, December.
    5. Thayer Alshaabi & David R Dewhurst & James P Bagrow & Peter S Dodds & Christopher M Danforth, 2021. "The sociospatial factors of death: Analyzing effects of geospatially-distributed variables in a Bayesian mortality model for Hong Kong," PLOS ONE, Public Library of Science, vol. 16(3), pages 1-20, March.
    6. 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.

    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:nas:journl:v:117:y:2020:p:13405-13412. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Eric Cain (email available below). General contact details of provider: http://www.pnas.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.