6 citations · 6 across the 4 of their papers we have counts for
4 papers
Prismo: A Decision Support System for Privacy-Preserving ML Framework Selection
Nges Brian Njungle, Eric Jahns, Luigi Mastromauro +3
Machine learning has become a crucial part of our lives, with applications spanning nearly every aspect of our daily activities. However, using personal information in machine lear…
FHEON: A Configurable Framework for Developing Privacy-Preserving Neural Networks Using Homomorphic Encryption
Nges Brian Njungle, Eric Jahns, Michel A. Kinsy
The widespread adoption of Machine Learning as a Service raises critical privacy and security concerns, particularly about data confidentiality and trust in both cloud providers an…
PrivSpike: Employing Homomorphic Encryption for Private Inference of Deep Spiking Neural Networks
Nges Brian Njungle, Eric Jahns, Milan Stojkov +1
Deep learning has become a cornerstone of modern machine learning. It relies heavily on vast datasets and significant computational resources for high performance. This data often…
AMAZE: Accelerated MiMC Hardware Architecture for Zero-Knowledge Applications on the Edge
Anees Ahmed, Nojan Sheybani, Davi Moreno +4
Collision-resistant, cryptographic hash (CRH) functions have long been an integral part of providing security and privacy in modern systems. Certain constructions of zero-knowledge…