Differentially Private Iterative Screening Rules for Linear Regression
Journal:
arXiv
Published Date:
Feb 25, 2025
Abstract
Linear $L_1$-regularized models have remained one of the simplest and most
effective tools in data science. Over the past decade, screening rules have
risen in popularity as a way to eliminate features when producing the sparse
regression weights of $L_1$ models. However, despite the increasing need of
privacy-preserving models for data analysis, to the best of our knowledge, no
differentially private screening rule exists. In this paper, we develop the
first private screening rule for linear regression. We initially find that this
screening rule is too strong: it screens too many coefficients as a result of
the private screening step. However, a weakened implementation of private
screening reduces overscreening and improves performance.
