Linear regression is a fundamental tool for statistical analysis, which has motivated the development of linear regression methods that satisfy provable privacy guarantees so that the learned model reveals little about any one data point used to construct it. Most existing privacy-preserving linear regression methods rely on the well-established framework of differential privacy, while the newly proposed PAC Privacy has not yet been explored in this context. In this paper, we systematically compare linear regression models trained with differential privacy and PAC privacy across three real-world datasets, observing several key findings that impact the performance of privacy-preserving linear regression.

翻译：线性回归是统计分析的基本工具，这推动了满足可证明隐私保证的线性回归方法的发展，使得学习到的模型几乎不泄露用于构建它的任何单个数据点。大多数现有的隐私保护线性回归方法依赖于已确立的差分隐私框架，而新提出的PAC隐私在此背景下尚未被探索。在本文中，我们系统比较了在三个真实世界数据集上使用差分隐私和PAC隐私训练的线性回归模型，观察到了影响隐私保护线性回归性能的几个关键发现。