Confidentiality Protection in the 2020 US Census of Population and Housing

TL;DR

This paper discusses implementing a differential privacy framework for the 2020 US Census to enhance confidentiality protection of detailed geographic and demographic data while maintaining controlled accuracy.

Contribution

It introduces a customized differential privacy approach specifically designed for the 2020 US Census to address modern confidentiality challenges.

Findings

Differential privacy effectively protects sensitive census data.

The approach balances data utility and confidentiality.

Enhanced privacy protection for granular geographic and demographic data.

Abstract

In an era where external data and computational capabilities far exceed statistical agencies' own resources and capabilities, they face the renewed challenge of protecting the confidentiality of underlying microdata when publishing statistics in very granular form and ensuring that these granular data are used for statistical purposes only. Conventional statistical disclosure limitation methods are too fragile to address this new challenge. This article discusses the deployment of a differential privacy framework for the 2020 US Census that was customized to protect confidentiality, particularly the most detailed geographic and demographic categories, and deliver controlled accuracy across the full geographic hierarchy.