# Robust and Private Learning of Halfspaces

arXiv: https://arxiv.org/abs/2011.14580 · HTML: https://mlsanity.com/paper/2011.14580/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Nov 2020.

## Built on by (most cited first, 7 of 7)

- [Robustness Implies Privacy in Statistical Estimation](https://mlsanity.com/paper/2212.05015/) (2022)
- [Covariance-Aware Private Mean Estimation Without Private Covariance Estimation](https://mlsanity.com/paper/2106.13329/) (2021)
- [From Robustness to Privacy and Back](https://mlsanity.com/paper/2302.01855/) (2023)
- [Learning to be adversarially robust and differentially private](https://mlsanity.com/paper/2201.02265/) (2022)
- [Algorithms with More Granular Differential Privacy Guarantees](https://mlsanity.com/paper/2209.04053/) (2022)
- [Robust and differentially private stochastic linear bandits](https://mlsanity.com/paper/2304.11741/) (2023)
- [Adapting to Linear Separable Subsets with Large-Margin in Differentially Private Learning](https://mlsanity.com/paper/2505.24737/) (2025)

## Abstract (arXiv)

In this work, we study the trade-off between differential privacy and adversarial robustness under L2-perturbations in the context of learning halfspaces. We prove nearly tight bounds on the sample complexity of robust private learning of halfspaces for a large regime of parameters. A highlight of our results is that robust and private learning is harder than robust or private learning alone. We complement our theoretical analysis with experimental results on the MNIST and USPS datasets, for a learning algorithm that is both differentially private and adversarially robust.