Machine learning for modular multiplication
Machine Learning
2024-03-01 v1 Cryptography and Security
Abstract
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.
Cite
@article{arxiv.2402.19254,
title = {Machine learning for modular multiplication},
author = {Kristin Lauter and Cathy Yuanchen Li and Krystal Maughan and Rachel Newton and Megha Srivastava},
journal= {arXiv preprint arXiv:2402.19254},
year = {2024}
}
Comments
14 pages, 12 figures. Comments welcome!