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Reconstruction of age distributions from differentially private census data.

Sigurd Dyrting1, Abraham Flaxman2, Ethan Sharygin3

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Population Research and Policy Review
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Statistical smoothing methods can improve the accuracy of noisy U.S. Census age distribution data. This enhances research utility while protecting privacy, crucial for understanding population dynamics.

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Area of Science:

  • Demography
  • Statistical Data Analysis
  • Population Studies

Background:

  • Population age distribution is vital for labor, services, and calculating age-specific rates (fertility, mortality).
  • The U.S. decennial census is a key data source, but new differential privacy methods (Disclosure Avoidance System - DAS) may reduce data utility for small areas and minority groups.
  • Concerns exist about the impact of data noise on the accuracy and reliability of demographic research.

Purpose of the Study:

  • To investigate statistical methods for enhancing the research utility of noisy U.S. Census age distribution data.
  • To assess if smoothing techniques can improve data fidelity without compromising privacy.
  • To explore the implications of these methods for future census data usage.

Main Methods:

  • Application of a non-parametric smoothing method with naive or informative priors.
  • Utilized demonstration data from the 2010 Census subjected to the U.S. Census Bureau's differential privacy implementation.
  • Focused on age distributions to evaluate the effectiveness of smoothing.

Main Results:

  • Smoothing age distributions demonstrated an increase in the fidelity of the demonstration data compared to previously published population counts by age.
  • The findings suggest that statistical smoothing can mitigate the negative impacts of noise introduced by differential privacy.
  • The study provides quantitative evidence for the effectiveness of smoothing in restoring data accuracy.

Conclusions:

  • Statistical smoothing methods offer a viable approach to enhance the utility of differentially private census data for research.
  • These techniques can help maintain data accuracy for demographic analysis, public health research, and disparity measurement.
  • The findings have significant implications for the interpretation and use of data from the 2020 U.S. Census and future data collection efforts.