Paper 2017/298 Original: eprint.iacr.org/2017/298 ↗
An Investigation of Sources of Randomness Within Discrete Gaussian Sampling
Abstract
This paper presents a performance and statistical analysis of random number generators and discrete Gaussian samplers implemented in software. Most Lattice-based cryptographic schemes utilise discrete Gaussian sampling and will require a quality random source. We examine a range of candidates for this purpose, including NIST DRBGs, stream ciphers and well-known PRNGs. The performance of these random sources is analysed within 64-bit implementations of Bernoulli, CDT and Ziggurat sampling. In addition we perform initial statistical testing of these samplers and include an investigation into improper seeding issues and their effect on the Gaussian samplers. Of the NIST approved Deterministic Random Bit Generators (DRBG), the AES based CTR-DRBG produced the best balanced performance in our tests.
Metadata
- Available format(s)
PDF- Category
- Implementation
- Publication info
- Preprint.
- History
- 2017-04-25: last updated
- 2017-04-07: published
- Short URL
- https://ia.cr/2017/298
- License
- https://creativecommons.org/licenses/by/4.0/
BibTeX
@misc{cryptoeprint:2017/298,
author = {Séamus Brannigan and Neil Smyth and Tobias Oder and Felipe Valencia and Elizabeth O’Sullivan and Tim Güneysu and Francesco Regazzoni},
title = {An Investigation of Sources of Randomness Within Discrete Gaussian Sampling},
howpublished = {Cryptology {ePrint} Archive, Paper 2017/298},
year = {2017},
url = {https://eprint.iacr.org/2017/298}
}