论文标题
通过二次采样和汇总的随机响应机制进行差异私有假设测试
Differentially Private Hypothesis Testing with the Subsampled and Aggregated Randomized Response Mechanism
论文作者
论文摘要
随机响应是分析机密数据的最古老，最著名的方法之一。但是，其用于差异化假设测试的实用性是有限的，因为它不能同时达到高隐私水平和低I型错误率。在本文中，我们展示了如何通过子样本和汇总技术克服这个问题。结果是一种通用方法，可用于频繁主义和贝叶斯测试。 {{我们在三种情况下说明了提案的性能：线性回归模型的拟合优度测试，Wilcoxon测试对位置参数的非参数测试以及非参数Kruskal-Wallis测试。
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.
