3 citations · 6 across the 8 of their papers we have counts for
8 papers
Atlas: Efficient Verifiable Semantic Search
Nikolay Avramov, Hidde Lycklama, Alexander Viand +1
Semantic search is a core primitive of modern applications, powering recommender systems, web search, and retrieval-augmented generation for language models. The provider controls…
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…
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…
Cohere: Managing Differential Privacy in Large Scale Systems
Nicolas Küchler, Emanuel Opel, Hidde Lycklama +2
The need for a privacy management layer in today's systems started to manifest with the emergence of new systems for privacy-preserving analytics and privacy compliance. As a resul…
RoFL: Robustness of Secure Federated Learning
Hidde Lycklama, Lukas Burkhalter, Alexander Viand +2
Even though recent years have seen many attacks exposing severe vulnerabilities in Federated Learning (FL), a holistic understanding of what enables these attacks and how they can…