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.
翻译:在一个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,它们在以非常颗粒的形式公布统计数据和确保这些颗粒数据仅用于统计目的时,面临着保护基本微观数据保密的新挑战。传统的统计披露限制方法太脆弱,无法应对这一新挑战。 文章讨论了2020年美国人口普查采用差别隐私框架的问题,这一框架是为保护保密而定制的,特别是最详细的地理和人口类别,并在整个地域等级中提供有控制的准确性。