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Machine learning for modular multiplication
March 1, 2024, 5:11 a.m. | Kristin Lauter, Cathy Yuanchen Li, Krystal Maughan, Rachel Newton, Megha Srivastava
cs.CR updates on arXiv.org arxiv.org
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.
applications arxiv cryptographic cs.cr cs.lg machine machine learning modular results
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