license: cc-by-4.0
tags:
- math
- cryptography
pretty_name: Datasets for Learning the Learning with Errors Problem
size_categories:
- 100M<n<1B
TAPAS: Datasets for Learning the Learning with Errors Problem
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.
The table below gives an overview of the datasets provided in this work:
| n | log q | omega | rho | # samples |
|---|---|---|---|---|
| 256 | 20 | 10 | 0.4284 | 400M |
| 512 | 12 | 10 | 0.9036 | 40M |
| 512 | 28 | 10 | 0.6740 | 40M |
| 512 | 41 | 10 | 0.3992 | 40M |
| 1024 | 26 | 10 | 0.8600 | 40M |