Machine learning for modular multiplication
Kristin Lauter Cathy Yuanchen Li Krystal Maughan Rachel Newton Megha Srivastava
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.