Machine learning for modular multiplication

Kristin Lauter, Cathy Yuanchen Li, Krystal Maughan, Rachel Newton, Megha Srivastava
2/29/2024
cs.LGcs.CR

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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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Cite this paper

@article{lauter2024machine,
  title  = {Machine learning for modular multiplication},
  author = {Kristin Lauter and Cathy Yuanchen Li and Krystal Maughan and Rachel Newton and Megha Srivastava},
  year   = {2024},
  eprint = {2402.19254},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2402.19254v1}
}

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