12 papers
Security Analysis for SCONE Logic Locking
Akashdeep Saha, Mohammed Nabeel, Johann Knechtel +2
SCONE [DAC'25] expands a logic locking interface with additional encoded inputs derived from the original primary inputs, and admits two realizations: a \textit{with-ES} variant, w…
PAPER: Privacy-Preserving Convolutional Neural Networks using Low-Degree Polynomial Approximations and Structural Optimizations on Leveled FHE
Eduardo Chielle, Manaar Alam, Jinting Liu +2
Recent work using Fully Homomorphic Encryption (FHE) has made non-interactive privacy-preserving inference of deep Convolutional Neural Networks (CNN) possible. However, the perfor…
CHEHAB RL: Learning to Optimize Fully Homomorphic Encryption Computations
Bilel Sefsaf, Abderraouf Dandani, Abdessamed Seddiki +4
Fully Homomorphic Encryption (FHE) enables computations directly on encrypted data, but its high computational cost remains a significant barrier. Writing efficient FHE code is a c…
@NTT: Algorithm-Targeted NTT hardware acceleration via Design-Time Constant Optimization
Mohammed Nabeel, Mahmoud Hafez, Michail Maniatakos
The Number Theoretic Transform (NTT) is a critical computational bottleneck in many lattice-based postquantum cryptographic (PQC) algorithms. By leveraging the Fast Fourier Transfo…
Fully Decentralized Certified Unlearning
Hithem Lamri, Michail Maniatakos
Machine unlearning (MU) seeks to remove the influence of specified data from a trained model in response to privacy requests or data poisoning. While certified unlearning has been…
BlendFL: Blended Federated Learning for Handling Multimodal Data Heterogeneity
Alejandro Guerra-Manzanares, Omar El-Herraoui, Michail Maniatakos +1
One of the key challenges of collaborative machine learning, without data sharing, is multimodal data heterogeneity in real-world settings. While Federated Learning (FL) enables mo…