| --- |
| license: cc-by-4.0 |
| tags: |
| - math |
| - cryptography |
| pretty_name: Datasets for Learning the Learning with Errors Problem |
| size_categories: |
| - 100M<n<1B |
| --- |
| |
| # 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 **t**oolkit for **a**nalysis of **p**ost-quantum cryptography using **A**I **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. |
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| 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 | |
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| ## Usage |
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| These datasets are intended to be used in conjunction with the code at: https://github.com/facebookresearch/LWE-benchmarking |
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| 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. |
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| Then, follow the instructions in this [README](https://github.com/facebookresearch/LWE-benchmarking/blob/main/README.md) to generate the full sets of LWE pairs and train AI models on this data. |
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| 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 chunk_ab.tar.gz to chunk_aj.tar.gz to download): http://dl.fbaipublicfiles.com/large_objects/lwe-benchmarking/n256_logq20/ |
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| Here are the exact links to each remaining section of the n=256 data (each link has 40M examples): |
| [1](http://dl.fbaipublicfiles.com/large_objects/lwe-benchmarking/n256_logq20/chunk_ab.tar.gz) |
| [2](http://dl.fbaipublicfiles.com/large_objects/lwe-benchmarking/n256_logq20/chunk_ac.tar.gz) |
| [3](http://dl.fbaipublicfiles.com/large_objects/lwe-benchmarking/n256_logq20/chunk_ad.tar.gz) |
| [4](http://dl.fbaipublicfiles.com/large_objects/lwe-benchmarking/n256_logq20/chunk_ae.tar.gz) |
| [5](http://dl.fbaipublicfiles.com/large_objects/lwe-benchmarking/n256_logq20/chunk_af.tar.gz) |
| [6](http://dl.fbaipublicfiles.com/large_objects/lwe-benchmarking/n256_logq20/chunk_ag.tar.gz) |
| [7](http://dl.fbaipublicfiles.com/large_objects/lwe-benchmarking/n256_logq20/chunk_ah.tar.gz) |
| [8](http://dl.fbaipublicfiles.com/large_objects/lwe-benchmarking/n256_logq20/chunk_ai.tar.gz) |
| [9](http://dl.fbaipublicfiles.com/large_objects/lwe-benchmarking/n256_logq20/chunk_aj.tar.gz) |
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