Differential Privacy in the 2020 Census Will Distort COVID-19 Rates

Mathew E. Hauer, Alexis R. Santos-Lozada

Research output: Contribution to journalArticlepeer-review

27 Scopus citations


Scholars rely on accurate population and mortality data to inform efforts regarding the coronavirus disease 2019 (COVID-19) pandemic, with age-specific mortality rates of high importance because of the concentration of COVID-19 deaths at older ages. Population counts, the principal denominators for calculating age-specific mortality rates, will be subject to noise infusion in the United States with the 2020 census through a disclosure avoidance system based on differential privacy. Using empirical COVID-19 mortality curves, the authors show that differential privacy will introduce substantial distortion in COVID-19 mortality rates, sometimes causing mortality rates to exceed 100 percent, hindering our ability to understand the pandemic. This distortion is particularly large for population groupings with fewer than 1,000 persons: 40 percent of all county-level age-sex groupings and 60 percent of race groupings. The U.S. Census Bureau should consider a larger privacy budget, and data users should consider pooling data to minimize differential privacy’s distortion.

Original languageEnglish (US)
StatePublished - 2021

All Science Journal Classification (ASJC) codes

  • General Social Sciences


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