Stephan Gnnemann

The paper shows that using random cropping, a standard data augmentation technique, can naturally amplify differential privacy guarantees for machine learning models without requiring any changes to the training process.

The paper proposes a novel framework using the primal-dual perspective of differential privacy to provide a unified, modular, and end-to-end robustness certification for complex machine learning models against joint backdoor attacks.

The paper introduces Indirect Harm Optimization (IHO), a novel black-box, adaptive, and efficient attack method that significantly improves jailbreak success rates against LLMs, aiming to provide a standardized evaluation benchmark for LLM robustness.