Small Area Modeling for Differentially Private Counts

Oregon Convention Center 

The Census Bureau adopted differential privacy (DP) as implemented through the TopDown Algorithm (TDA) for the 2020 Decennial Census in order to protect respondent confidentiality. Though the variances of the additive DP noise are publicly available, the impacts of postprocessing in the TDA to ensure various quality metrics, such as hierarchical consistency and non-negativity are met are less easily quantified as the unprotected counts are not publicly available for 2020 data. In this work, we investigate the use of a small area estimation approach to strengthen estimates of variability obtained using the 2010 demonstration products, as compared to the official 2010 redistricting file. We propose using a grouping of similar geographies to obtain estimates of variance from the 2010 data, and to incorporate these updated variance estimates to improve the estimates for 2020.

Small Area Estimation

Differential Privacy

Generalized variance function 

Survey Research Methods Section