How Differential Privacy Will Affect Estimates of Air Pollution Exposure and Disparities in the United States

Abstract

Census data is crucial to understand energy and environmental justice outcomes such as poor air quality which disproportionately impact people of color in the U.S. Wwith the advent of sophisticated personal datasets and analysis, Census Bureau is considering adding top-down noise (differential privacy) and post-processing 2020 census data to reduce the risk of identification of individual respondents. Using 2010 demonstration census and pollution data, I find that compared to the original census, differentially private (DP) census significantly changes ambient pollution exposure in areas with sparse populations. White Americans have lowest variability, followed by Latinos, Asian, and Black Americans. DP underestimates pollution disparities for SO2 and PM2.5 while overestimates the pollution disparities for PM10.