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license: cc-by-4.0 |
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tags: |
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- math |
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- cryptography |
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pretty_name: Datasets for Learning the Learning with Errors Problem |
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size_categories: |
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- 100M<n<1B |
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--- |
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# TAPAS: Datasets for Learning the Learning with Errors Problem |
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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 **t**oolkit for **a**nalysis of **p**ost-quantum cryptography using **A**I **s**ystems. These datasets cover several LWE settings and can be used off-the-shelf by AI practitioners to prototype new approaches to cracking LWE. |
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The table below gives an overview of the datasets provided in this work: |
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| n | log q | omega | rho | # samples | |
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|--------|-----------|----------|--------|------------| |
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| 256 | 20 | 10 | 0.4284 | 400M | |
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| 512 | 12 | 10 | 0.9036 | 40M | |
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| 512 | 28 | 10 | 0.6740 | 40M | |
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| 512 | 41 | 10 | 0.3992 | 40M | |
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| 1024 | 26 | 10 | 0.8600 | 40M | |
