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.

中文翻译：

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2020 年美国人口和住房普查中的保密性保护


在外部数据和计算能力远远超过统计机构自身资源和能力的时代，他们面临着新的挑战，即在以非常精细的形式发布统计数据时保护底层微数据的机密性，并确保这些精细数据仅用于统计目的。传统的统计披露限制方法太脆弱，无法应对这一新挑战。本文讨论了 2020 年美国人口普查的差分隐私框架的部署，该框架经过定制以保护机密性，特别是最详细的地理和人口统计类别，并在整个地理层次结构中提供受控的准确性。