3 papers
cs.CR2025
Artemis: Efficient Commit-and-Prove SNARKs for zkML
Hidde Lycklama, Alexander Viand, Nikolay Avramov +2
Ensuring that AI models are both verifiable and privacy-preserving is important for trust, accountability, and compliance. To address these concerns, recent research has focused on…
cs.CR2025
DPolicy: Managing Privacy Risks Across Multiple Releases with Differential Privacy
Nicolas Küchler, Alexander Viand, Hidde Lycklama +1
Differential Privacy (DP) has emerged as a robust framework for privacy-preserving data releases and has been successfully applied in high-profile cases, such as the 2020 US Census…
cs.CR2024
Holding Secrets Accountable: Auditing Privacy-Preserving Machine Learning
Hidde Lycklama, Alexander Viand, Nicolas Küchler +2
Recent advancements in privacy-preserving machine learning are paving the way to extend the benefits of ML to highly sensitive data that, until now, have been hard to utilize due t…