# Confidentiality Protection in the 2020 US Census of Population and Housing

arXiv: https://arxiv.org/abs/2206.03524 · HTML: https://mlsanity.com/paper/2206.03524/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jun 2022.

## Built on by (most cited first, 5 of 5)

- [Confidence-Ranked Reconstruction of Census Microdata from Published Statistics](https://mlsanity.com/paper/2211.03128/) (2022)
- [An Examination of the Alleged Privacy Threats of Confidence-Ranked Reconstruction of Census Microdata](https://mlsanity.com/paper/2311.03171/) (2023)
- [Evaluating utility in synthetic banking microdata applications](https://mlsanity.com/paper/2410.22519/) (2024)
- [Generate-then-Verify: Reconstructing Data from Limited Published Statistics](https://mlsanity.com/paper/2504.21199/) (2025)
- [On the Inherent Privacy Amplification of Missing Data](https://mlsanity.com/paper/2602.01928/) (2026)

## Abstract (arXiv)

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.