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How differential privacy will affect our understanding of population growth in the United States

Author

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  • Santos-Lozada, Alexis R

    (Pennsylvania State University)

  • Perez-Rivera, Danilo T
  • Bhat, Aarti C.

Abstract

The implementation of a proposed differential privacy algorithm to 2020 US Census data releases, and other census products has brought about discussions about the consistency and reliability of the data produced under the proposed disclosure avoidance system. We test the potential impact of this change in disclosure avoidance systems to the tracking of population growth and distribution using county-level population counts. We ask how population counts produced under the differential privacy algorithm might lead to different conclusions regarding population growth for the total population and three major racial/ethnic groups in comparison to counts produced using the traditional methods. Our results suggest that the implementation of differential privacy, as proposed, will impact our understanding of population changes in the US. We find potential for overstating and understating growth and decline, with these effects being more pronounced for non-Hispanic blacks and Hispanics, as well as for non-metropolitan counties. These findings draw attention to the potential local consequences of the implementation of differential privacy for tracking demographic changes of the US population, which is bound to have implication for our understanding of the transformations the nation is going through.

Suggested Citation

  • 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.
  • Handle: RePEc:osf:socarx:pmux7
    DOI: 10.31219/osf.io/pmux7
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    Cited by:

    1. 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.
    2. 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.
    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.

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