TAPAS: Datasets for Learning the Learning with Errors Problem

About this Data

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

Usage

These datasets are intended to be used in conjunction with the code at: https://github.com/facebookresearch/LWE-benchmarking

Download and unzip the .tar.gz files into a directory with enough storage. For the datasets split into different chunks, concatenate all the files into one data.prefix file after unzipping.

Then, follow the instructions in this README to generate the full sets of LWE pairs and train AI models on this data.