论文标题
2020年美国人口普查的机密保护
Confidentiality Protection in the 2020 US Census of Population and Housing
论文作者
论文摘要
在外部数据和计算能力远远超过统计机构自己的资源和能力的时代，他们面临着重新挑战的挑战，即在以非常颗粒状的形式发布统计数据时保护基本微数据的机密性，并确保这些颗粒数据仅用于统计目的。常规的统计披露限制方法太脆弱了，无法应对这一新挑战。本文讨论了针对2020年美国人口普查的差异隐私框架的部署，该框架旨在保护机密性，尤其是最详细的地理和人口类别，并在整个地理层次结构中提供控制的准确性。
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.
