PRECAD: Privacy-Preserving and Robust Federated Learning via Crypto-Aided Differential Privacy

no code implementations • 22 Oct 2021 • Xiaolan Gu, Ming Li, Li Xiong

In this paper, we develop a framework called PRECAD, which simultaneously achieves differential privacy (DP) and enhances robustness against model poisoning attacks with the help of cryptography.

Federated Learning Model Poisoning +1

Two Birds, One Stone: Achieving both Differential Privacy and Certified Robustness for Pre-trained Classifiers via Input Perturbation

no code implementations • 29 Sep 2021 • Pengfei Tang, Wenjie Wang, Xiaolan Gu, Jian Lou, Li Xiong, Ming Li

To solve this challenge, a reconstruction network is built before the public pre-trained classifiers to offer certified robustness and defend against adversarial examples through input perturbation.

Image Classification