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 $\mathbf{t}$oolkit for $\mathbf{a}$nalysis of $\mathbf{p}$ost-quantum cryptography using $\mathbf{A}$I $\mathbf{s}$ystems. These datasets cover several LWE settings and can be used off-the-shelf by AI practitioners to prototype new approaches to cracking LWE.

This work documents TAPAS dataset creation, establishes attack performance baselines, and lays out directions for future work.