Summary of Machine Learning For Modular Multiplication, by Kristin Lauter et al.
Machine learning for modular multiplication
by Kristin Lauter, Cathy Yuanchen Li, Krystal Maughan, Rachel Newton, Megha Srivastava
First submitted to arxiv on: 29 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Cryptography and Security (cs.CR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper explores two machine learning approaches, circular regression and a sequence-to-sequence transformer model, to tackle modular multiplication, a crucial task in cryptographic applications. While both methods showed limited success, the results provide evidence for the difficulty of tasks involving modular multiplication, which is fundamental to many cryptosystems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers study two AI techniques to solve modular multiplication problems, which are important for secure online transactions. They try circular regression and a special kind of neural network called a sequence-to-sequence model. Unfortunately, both methods didn’t do as well as expected, suggesting that these tasks might be really hard. This is bad news for cryptography. |
Keywords
* Artificial intelligence * Machine learning * Neural network * Regression * Sequence model * Transformer
