TAPAS: Datasets for Learning the Learning with Errors Problem
This work addresses a data bottleneck for AI practitioners studying LWE attacks, though it is incremental as it focuses on dataset creation rather than new attack methods.
The paper tackles the lack of accessible data for AI research on Learning with Errors (LWE) attacks in post-quantum cryptography by introducing the TAPAS datasets, which enable off-the-shelf prototyping and establish performance baselines.
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.