透明隐私是有原则的隐私
Transparent Privacy is Principled Privacy
摘要：
在一个技术性的处理，这篇文章建立了透明的隐私的必要性，为绘制无偏见的统计推断的广泛的科学问题。透明性是差异隐私的一个显著特征：数据私有化的概率机制可以在不破坏隐私保证的情况下公开。从总的调查误差的角度来看，由于透明隐私的不确定性可以被认为是一个动态的和可控的组件。由于2020年美国十年一次的人口普查采用了差异隐私，通过优化对私有化数据产品施加的限制对透明度构成了威胁，并导致统计可用性有限。透明隐私为从私有化数据发布中进行原则性推理提供了一条可行的途径，并在现代数据管理中显示出改善可重复性、问责制和公众信任的巨大希望。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
