6 papers
Towards Deep Encrypted Training: Low-Latency, Memory-Efficient, and High-Throughput Inference for Privacy-Preserving Neural Networks
Nges Brian Njungle, Eric Jahns, Michel A. Kinsy
Privacy-preserving machine learning (PPML) has become increasingly important in applications where sensitive data must remain confidential. Homomorphic Encryption (HE) enables comp…
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…
Learning Neuron Dynamics within Deep Spiking Neural Networks
Eric Jahns, Davi Moreno, Michel A. Kinsy
Spiking Neural Networks (SNNs) offer a promising energy-efficient alternative to Artificial Neural Networks (ANNs) by utilizing sparse and asynchronous processing through discrete…
Discretized Quadratic Integrate-and-Fire Neuron Model for Deep Spiking Neural Networks
Eric Jahns, Davi Moreno, Milan Stojkov +1
Spiking Neural Networks (SNNs) have emerged as energy-efficient alternatives to traditional artificial neural networks, leveraging asynchronous and biologically inspired neuron dyn…
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…