100 citations · 138 across the 13 of their papers we have counts for
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cs.CR2024
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.CR2024★ 1 cited
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…