Easy Differentially Private Linear Regression. (arXiv:2208.07353v2 [cs.LG] UPDATED)

Linear regression is a fundamental tool for statistical analysis. This has
motivated the development of linear regression methods that also satisfy
differential privacy and thus guarantee that the learned model reveals little
about any one data point used to construct it. However, existing differentially
private solutions assume that the end user can easily specify good data bounds
and hyperparameters. Both present significant practical obstacles. In this
paper, we study an algorithm which uses the exponential mechanism to select a
model …

algorithm analysis data development differential privacy end end user guarantee point privacy private solutions study tool