The Impact of De-Identification on Single-Year-of-Age Counts in the U.S. Census

TL;DR

This paper evaluates the effects of differential privacy versus data swapping on census age data accuracy, especially for small districts, finding DP generally offers comparable or better accuracy and transparency.

Contribution

It provides a comparative analysis of DP and swapping impacts on census age counts, highlighting DP's advantages for small districts and its suitability for school planning.

Findings

DP offers comparable or improved accuracy over swapping for small districts.

Both DP and swapping exhibit poor behavior in small districts, but DP maintains better transparency.

DP is recommended for single-year-of-age counts in school planning contexts.

Abstract

In 2020, the U.S. Census Bureau transitioned from data swapping to differential privacy (DP) in its approach to de-identifying decennial census data. This decision has faced considerable criticism from data users, particularly due to concerns about the accuracy of DP. We compare the relative impacts of swapping and DP on census data, focusing on the use case of school planning, where single-year-of-age population counts (i.e., the number of four-year-olds in the district) are used to estimate the number of incoming students and make resulting decisions surrounding faculty, classrooms, and funding requests. We examine these impacts for school districts of varying population sizes and age distributions. Our findings support the use of DP over swapping for single-year-of-age counts; in particular, concerning behaviors associated with DP (namely, poor behavior for smaller districts) occur with swapping mechanisms as well. For the school planning use cases we investigate, DP provides comparable, if not improved, accuracy over swapping, while offering other benefits such as improved transparency.