论文标题
部分可观测时空混沌系统的无模型预测
Bayesian and Frequentist Semantics for Common Variations of Differential Privacy: Applications to the 2020 Census
论文作者
论文摘要
本文的目的是指导对语义隐私保证的解释，以保证差异隐私的某些主要变化，其中包括纯，近似，rényi，零集中和$ f $ dixinal的隐私。我们解释了隐私会计参数，常见语义和贝叶斯语义（包括新的结果）。驾驶申请是对2020年人口普查公法的机密保护措施的解释94-171重新划分数据摘要文件，于2021年8月12日发布，该文件首次由正式的隐私保证生产。
The purpose of this paper is to guide interpretation of the semantic privacy guarantees for some of the major variations of differential privacy, which include pure, approximate, Rényi, zero-concentrated, and $f$ differential privacy. We interpret privacy-loss accounting parameters, frequentist semantics, and Bayesian semantics (including new results). The driving application is the interpretation of the confidentiality protections for the 2020 Census Public Law 94-171 Redistricting Data Summary File released August 12, 2021, which, for the first time, were produced with formal privacy guarantees.
