Confidentiality Protection in the 2020 US Census of Population and Housing

John Abowd () and Michael B Hawes

Papers from arXiv.org

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.

Date: 2022-06, Revised 2022-12
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Published in Annual Review of Statistics and Its Application 2023 10:1

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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2206.03524

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