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arXiv · 2402.19254

Machine learning for modular multiplication

Kristin Lauter·Cathy Yuanchen Li·Krystal Maughan·Rachel Newton·Megha Srivastava
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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.

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Kristin Lauter, Cathy Yuanchen Li, Krystal Maughan, Rachel Newton, Megha Srivastava. 2024-02-29. Machine learning for modular multiplication. https://arxiv.org/abs/2402.19254

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