Confidentiality Protection in the 2020 US Census of Population and Housing
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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.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). First posted on arXiv 7 Jun 2022.
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Citing papers per year: 2022: 1; 2023: 1; 2024: 1; 2025: 1; 2026: 1.
5 papers.
- 2022Confidence-Ranked Reconstruction of Census Microdata from Published Statisticscited by 16
- 2023An Examination of the Alleged Privacy Threats of Confidence-Ranked Reconstruction of Census Microdatacited by 0
- 2024Evaluating utility in synthetic banking microdata applicationscited by 0
- 2025Generate-then-Verify: Reconstructing Data from Limited Published Statisticscited by 0
- 2026On the Inherent Privacy Amplification of Missing Datacited by 0
- 2026On the Inherent Privacy Amplification of Missing Data2 Feb 2026
- 2025Generate-then-Verify: Reconstructing Data from Limited Published Statistics29 Apr 2025
- 2024Evaluating utility in synthetic banking microdata applications29 Oct 2024
- 2023An Examination of the Alleged Privacy Threats of Confidence-Ranked Reconstruction of Census Microdata6 Nov 2023
- 2022Confidence-Ranked Reconstruction of Census Microdata from Published Statistics6 Nov 2022