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.
- Pub Date:
- November 2020
- Computer Science - Machine Learning;
- Computer Science - Cryptography and Security;
- Computer Science - Data Structures and Algorithms;
- Statistics - Machine Learning
- AISTATS 2021