Salsa Fresca: Angular Embeddings and Pre-Training for ML Attacks on Learning With Errors
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866911769602031616 |
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| author | Stevens, Samuel Wenger, Emily Li, Cathy Nolte, Niklas Saxena, Eshika Charton, François Lauter, Kristin |
| author_facet | Stevens, Samuel Wenger, Emily Li, Cathy Nolte, Niklas Saxena, Eshika Charton, François Lauter, Kristin |
| contents | Learning with Errors (LWE) is a hard math problem underlying recently standardized post-quantum cryptography (PQC) systems for key exchange and digital signatures. Prior work proposed new machine learning (ML)-based attacks on LWE problems with small, sparse secrets, but these attacks require millions of LWE samples to train on and take days to recover secrets. We propose three key methods -- better preprocessing, angular embeddings and model pre-training -- to improve these attacks, speeding up preprocessing by $25\times$ and improving model sample efficiency by $10\times$. We demonstrate for the first time that pre-training improves and reduces the cost of ML attacks on LWE. Our architecture improvements enable scaling to larger-dimension LWE problems: this work is the first instance of ML attacks recovering sparse binary secrets in dimension $n=1024$, the smallest dimension used in practice for homomorphic encryption applications of LWE where sparse binary secrets are proposed. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_01082 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Salsa Fresca: Angular Embeddings and Pre-Training for ML Attacks on Learning With Errors Stevens, Samuel Wenger, Emily Li, Cathy Nolte, Niklas Saxena, Eshika Charton, François Lauter, Kristin Cryptography and Security Machine Learning Learning with Errors (LWE) is a hard math problem underlying recently standardized post-quantum cryptography (PQC) systems for key exchange and digital signatures. Prior work proposed new machine learning (ML)-based attacks on LWE problems with small, sparse secrets, but these attacks require millions of LWE samples to train on and take days to recover secrets. We propose three key methods -- better preprocessing, angular embeddings and model pre-training -- to improve these attacks, speeding up preprocessing by $25\times$ and improving model sample efficiency by $10\times$. We demonstrate for the first time that pre-training improves and reduces the cost of ML attacks on LWE. Our architecture improvements enable scaling to larger-dimension LWE problems: this work is the first instance of ML attacks recovering sparse binary secrets in dimension $n=1024$, the smallest dimension used in practice for homomorphic encryption applications of LWE where sparse binary secrets are proposed. |
| title | Salsa Fresca: Angular Embeddings and Pre-Training for ML Attacks on Learning With Errors |
| topic | Cryptography and Security Machine Learning |
| url | https://arxiv.org/abs/2402.01082 |