2 papers
cs.CR2026
Certified in Theory, Broken in Practice: Assumption Gaps in Cryptographic Model Certification
Carter Luck, Olive Franzese-McLaughlin, Elisaweta Masserova +3
Privacy-preserving machine learning auditing protocols allow auditors to assess models for properties such as accuracy or fairness, without revealing their internals or training da…
cs.CR2026
ZKBoost: Zero-Knowledge Verifiable Training for XGBoost
Nikolas Melissaris, Antigoni Polychroniadou, Akira Takahashi +2
Gradient boosted decision trees, particularly XGBoost, are among the most effective methods for tabular data. As deployment in sensitive settings increases, cryptographic guarantee…