8 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…
DALC-CT: Dynamic Analysis of Low-Level Code Traces for Constant-Time Verification
Nges Brian Njungle, Edwin P. Kayang, Mishel J. Paul +1
Timing side-channel attacks exploit variations in program execution time to recover sensitive information. Cryptographic implementations are especially vulnerable to these attacks,…
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…
Gotta Hash 'Em All! Speeding Up Hash Functions for Zero-Knowledge Proof Applications
Nojan Sheybani, Tengkai Gong, Anees Ahmed +3
Collision-resistant cryptographic hash functions (CRHs) are crucial for security, particularly for message authentication in Zero-knowledge Proof (ZKP) applications. However, tradi…