Private Linear Regression with Differential Privacy and PAC Privacy
This work addresses privacy concerns in statistical analysis for data scientists, but it is incremental as it applies existing privacy frameworks to linear regression.
The paper tackles the problem of training linear regression models with privacy guarantees, comparing differential privacy and PAC privacy on three real-world datasets to identify key performance impacts.
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.