Machine learning for modular multiplication

Fuente: arXiv
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Auteurs principaux: Lauter, Kristin, Li, Cathy Yuanchen, Maughan, Krystal, Newton, Rachel, Srivastava, Megha
Format: Preprint
Publié: 2024
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author Lauter, Kristin
Li, Cathy Yuanchen
Maughan, Krystal
Newton, Rachel
Srivastava, Megha
author_facet Lauter, Kristin
Li, Cathy Yuanchen
Maughan, Krystal
Newton, Rachel
Srivastava, Megha
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2402_19254
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Machine learning for modular multiplication
Lauter, Kristin
Li, Cathy Yuanchen
Maughan, Krystal
Newton, Rachel
Srivastava, Megha
Machine Learning
Cryptography and Security
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.
title Machine learning for modular multiplication
topic Machine Learning
Cryptography and Security
url https://arxiv.org/abs/2402.19254