对比差分隐私与PAC隐私在线性回归中的效果，发现两者各有优劣。
Private Linear Regression with Differential Privacy and PAC Privacy
- 在真实数据集上比较差分隐私与PAC隐私的线性回归方法
- 发现两种隐私机制在不同数据集上的性能差异显著
- 为隐私保护回归模型选择提供实证依据，适合隐私研究者
线性回归是统计分析的基础工具，推动了具备可证明隐私保障的回归方法发展，使得学习到的模型对任一数据点泄露信息极少。现有大多数隐私保护线性回归方法依赖成熟的差分隐私框架，而新提出的PAC隐私尚未在此领域被系统探索。本文在三个真实数据集上系统比较了采用差分隐私和PAC隐私训练的线性回归模型，观察到若干影响隐私保护回归性能的关键发现。
原文摘要 · Abstract (English)
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.
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