2 papers
cs.CR2026
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…
cs.CR2026
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…