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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2510.08797 |
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| _version_ | 1866911328758661120 |
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| author | Saxena, Eshika Alfarano, Alberto Charton, François Wenger, Emily Lauter, Kristin |
| author_facet | Saxena, Eshika Alfarano, Alberto Charton, François Wenger, Emily Lauter, Kristin |
| contents | AI-powered attacks on Learning with Errors (LWE), an important hard math problem in post-quantum cryptography, rival or outperform "classical" attacks on LWE under certain parameter settings. Despite the promise of this approach, a dearth of accessible data limits AI practitioners' ability to study and improve these attacks. Creating LWE data for AI model training is time- and compute-intensive and requires significant domain expertise. To fill this gap and accelerate AI research on LWE attacks, we propose the TAPAS datasets, a Toolkit for Analysis of Post-quantum cryptography using AI Systems. These datasets cover several LWE settings and can be used off-the-shelf by AI practitioners to prototype new approaches to cracking LWE. This work documents TAPAS dataset creation, establishes attack performance baselines, and lays out directions for future work. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_08797 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | TAPAS: Datasets for Learning the Learning with Errors Problem Saxena, Eshika Alfarano, Alberto Charton, François Wenger, Emily Lauter, Kristin Machine Learning Cryptography and Security AI-powered attacks on Learning with Errors (LWE), an important hard math problem in post-quantum cryptography, rival or outperform "classical" attacks on LWE under certain parameter settings. Despite the promise of this approach, a dearth of accessible data limits AI practitioners' ability to study and improve these attacks. Creating LWE data for AI model training is time- and compute-intensive and requires significant domain expertise. To fill this gap and accelerate AI research on LWE attacks, we propose the TAPAS datasets, a Toolkit for Analysis of Post-quantum cryptography using AI Systems. These datasets cover several LWE settings and can be used off-the-shelf by AI practitioners to prototype new approaches to cracking LWE. This work documents TAPAS dataset creation, establishes attack performance baselines, and lays out directions for future work. |
| title | TAPAS: Datasets for Learning the Learning with Errors Problem |
| topic | Machine Learning Cryptography and Security |
| url | https://arxiv.org/abs/2510.08797 |