LGFeb 29, 2024
Machine learning for modular multiplicationKristin Lauter, Cathy Yuanchen Li, Krystal Maughan et al.
Motivated by cryptographic applications, we investigate two machine learning approaches to modular multiplication: namely circular regression and a sequence-to-sequence transformer model. The limited success of both methods demonstrated in our results gives evidence for the hardness of tasks involving modular multiplication upon which cryptosystems are based.