# Differentially Private Iterative Screening Rules for Linear Regression

arXiv: https://arxiv.org/abs/2502.18578 · HTML: https://mlsanity.com/paper/2502.18578/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 33 references that are in the map.
- First posted on arXiv 25 Feb 2025.

## Builds on (most cited first, 6 of 6)

- [Privacy enabled Financial Text Classification using Differential Privacy and Federated Learning](https://mlsanity.com/paper/2110.01643/) (2021)
- [A Safe Screening Rule for Sparse Logistic Regression](https://mlsanity.com/paper/1307.4145/) (2013)
- [Scaling Up Differentially Private LASSO Regularized Logistic Regression via Faster Frank-Wolfe Iterations](https://mlsanity.com/paper/2310.19978/) (2023)
- [Differentially Private Model Selection with Penalized and Constrained Likelihood](https://mlsanity.com/paper/1607.04204/) (2016)
- [Differential Privacy for Sparse Classification Learning](https://mlsanity.com/paper/1908.00780/) (2019)
- [Feature Selection from Differentially Private Correlations](https://mlsanity.com/paper/2408.10862/) (2024)

## Built on by (most cited first, 2 of 2)

- [Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack](https://mlsanity.com/paper/2507.05441/) (2025)
- [Bayesian Meta-Analyses Could Be More: A Case Study in Trial of Labor After a Cesarean-section Outcomes and Complications](https://mlsanity.com/paper/2601.10089/) (2026)

## Abstract (arXiv)

Linear L1-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 L1 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.