Reviews: Differentially Private Bayesian Linear Regression

This paper is methodological (and experimental) in nature, providing a suite of approaches to differentially-private Bayesian linear regression. The key significance is to revisit DP linear regression in the Bayesian setting, where it is natural to consider 1) how privacy-preserving noise affects posterior estimates; 2) leverage Bayesian inference through directly modelling the noise process, to improve utility (broadly construed including in terms of calibration). The paper does a quality job of exploring how such modelling and inference could be performed based on sufficient statistic perturbation. The paper has high clarity, further adding to the potential practical impact. The main technical ideas are largely inspired by prior work such as Bernstein and Sheldon (2018)'s work on exponential families.