100 citations · 142 across the 16 of their papers we have counts for
4 papers · 1 filter
Fragile Giants: Understanding the Susceptibility of Models to Subpopulation Attacks
Isha Gupta, Hidde Lycklama, Emanuel Opel +2
As machine learning models become increasingly complex, concerns about their robustness and trustworthiness have become more pressing. A critical vulnerability of these models is d…
UTrace: Poisoning Forensics for Private Collaborative Learning
Evan Rose, Hidde Lycklama, Harsh Chaudhari +3
Privacy-preserving machine learning (PPML) systems enable multiple data owners to collaboratively train models without revealing their raw, sensitive data by leveraging cryptograph…
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…
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…