论文标题
差异私人置信区间的参数bootstrap
Parametric Bootstrap for Differentially Private Confidence Intervals
论文作者
论文摘要
本文的目的是开发一种实用和通用的方法来构建置信区间，以进行不同的私有参数估计。我们发现参数引导程序是一个简单有效的解决方案。它可以清楚地理解数据样本和随机隐私机制的可变性，并将“开箱即用”应用于广泛的私人估计例程。它还可以帮助纠正剪辑数据引起的偏差以限制灵敏度。我们证明，参数引导程序在两个广泛相关的设置中给出了一致的置信区间，包括对线性回归的新型改编，避免了多次访问协变量数据。我们证明了其对各种估计器的有效性，并发现它即使在样本量适中，它也提供良好的覆盖范围，并且比替代方法更好。
The goal of this paper is to develop a practical and general-purpose approach to construct confidence intervals for differentially private parametric estimation. We find that the parametric bootstrap is a simple and effective solution. It cleanly reasons about variability of both the data sample and the randomized privacy mechanism and applies "out of the box" to a wide class of private estimation routines. It can also help correct bias caused by clipping data to limit sensitivity. We prove that the parametric bootstrap gives consistent confidence intervals in two broadly relevant settings, including a novel adaptation to linear regression that avoids accessing the covariate data multiple times. We demonstrate its effectiveness for a variety of estimators, and find that it provides confidence intervals with good coverage even at modest sample sizes and performs better than alternative approaches.
