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Auteurs principaux:	Lauter, Kristin, Li, Cathy Yuanchen, Maughan, Krystal, Newton, Rachel, Srivastava, Megha
Format:	Preprint
Publié:	2024
Sujets:	Machine Learning Cryptography and Security
Accès en ligne:	https://arxiv.org/abs/2402.19254
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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.