14 citations · 20 across the 6 of their papers we have counts for
6 papers · 1 filter
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…
Architectural Backdoors for Within-Batch Data Stealing and Model Inference Manipulation
Nicolas Küchler, Ivan Petrov, Conrad Grobler +1
For nearly a decade the academic community has investigated backdoors in neural networks, primarily focusing on classification tasks where adversaries manipulate the model predicti…
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…
Zeph: Cryptographic Enforcement of End-to-End Data Privacy
Lukas Burkhalter, Nicolas Küchler, Alexander Viand +2
As increasingly more sensitive data is being collected to gain valuable insights, the need to natively integrate privacy controls in data analytics frameworks is growing in importa…
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…