TAPAS: Datasets for Learning the Learning with Errors Problem
Eshika Saxena, Alberto Alfarano, Francois Charton, Emily Wenger, Kristin E. Lauter
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 ${\bf t}$oolkit for ${\bf a}$nalysis of ${\bf p}$ost-quantum cryptography using ${\bf A}$I ${\bf s}$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.