Randomized response is one of the oldest and most well-known methods for analyzing confidential data. However, its utility for differentially private hypothesis testing is limited because it cannot achieve high privacy levels and low type I error rates simultaneously. In this article, we show how to overcome this issue with the subsample and aggregate technique. The result is a general-purpose method that can be used for both frequentist and Bayesian testing. {{We illustrate the performance of our proposal in three scenarios: goodness-of-fit testing for linear regression models, nonparametric testing of a location parameter with the Wilcoxon test, and the nonparametric Kruskal-Wallis test.

翻译：随机响应是分析机密数据最古老且最著名的方法之一。然而，其在差分隐私假设检验中的效用受到限制，因为该机制无法同时实现高隐私水平与低第一类错误率。本文展示了如何通过子采样与聚合技术克服这一问题，得到一种可用于频率学派与贝叶斯检验的通用方法。我们通过三个场景说明了所提方法的性能：线性回归模型的拟合优度检验、基于Wilcoxon检验的位置参数非参数检验，以及非参数Kruskal-Wallis检验。