论文标题
透明隐私是原则上的隐私
Transparent Privacy is Principled Privacy
论文作者
论文摘要
在技术治疗中，本文确定了透明隐私的必要性，以对广泛的科学问题提出无偏统计的推断。透明度是差异隐私所享有的独特功能：可以将数据私有化的概率机制公开而不破坏隐私保证。从总调查错误的角度来看，由于透明隐私而引起的不确定性可能被认为是动态和可控的组件。随着2020年美国十年型人口普查采用差异隐私，通过优化对私有化数据产品施加的限制构成了对透明度的威胁，并导致统计可用性有限。透明的隐私提出了从私有化数据发布的原则推论的可行途径，并在改善现代数据策划的可重复性，问责制和公众信任方面表现出了巨大的希望。
In a technical treatment, this article establishes the necessity of transparent privacy for drawing unbiased statistical inference for a wide range of scientific questions. Transparency is a distinct feature enjoyed by differential privacy: the probabilistic mechanism with which the data are privatized can be made public without sabotaging the privacy guarantee. Uncertainty due to transparent privacy may be conceived as a dynamic and controllable component from the total survey error perspective. As the 2020 U.S. Decennial Census adopts differential privacy, constraints imposed on the privatized data products through optimization constitute a threat to transparency and result in limited statistical usability. Transparent privacy presents a viable path toward principled inference from privatized data releases, and shows great promise toward improved reproducibility, accountability, and public trust in modern data curation.
