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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2402.19254 |
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| _version_ | 1866909123420880896 |
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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 |