14 papers
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…
Asymptotic Optimality of the High-Dimensional Gaussian Mechanism and Improved Low-Dimensional Mechanisms for Differential Privacy
Yu Wei, Alexander Bienstock, Antigoni Polychroniadou
The additive noise mechanism is a foundational tool for differential privacy (DP) of -dimensional real-valued vector queries. The Gaussian mechanism, utilizing Gaussian noise, i…
Revisiting ML Training under Fully Homomorphic Encryption: Convergence Guarantees, Differential Privacy, and Efficient Algorithms
Yvonne Zhou, Mingyu Liang, Ivan Brugere +5
We present the first theoretical convergence analysis of machine learning training under fully homomorphic encryption (FHE), combined with a differentially private (DP) training al…
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…
AgentCrypt: Advancing Privacy and (Secure) Computation in AI Agent Collaboration
Harish Karthikeyan, Yue Guo, Leo de Castro +5
As AI agents increasingly operate in complex environments, ensuring reliable, context-aware privacy is critical for regulatory compliance. Traditional access controls are insuffici…
Scalable Secure Biometric Authentication without Auxiliary Identifiers
Alexander Bienstock, Daniel Escudero, Antigoni Polychroniadou +5
The prevalence of biometric authentication has been on the rise due to its ease of use and elimination of weak passwords. To date, most biometric authentication systems have been d…