facebook/TAPAS

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.

Due to storage constraints, we only provide 40M of the n=256 data here on huggingface. The rest can be found at this directory (append filenames ranging from chunkab.tar.gz to chunkaj.tar.gz to download): http://dl.fbaipublicfiles.com/largeobjects/lwe-benchmarking/n256logq20/

Here are the exact links to each remaining section of the n=256 data (each link has 40M examples): 1 2 3 4 5 6 7 8 9