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Random Number Generators and Seeding for Differential Privacy

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arxiv 2307.03543 v1 pith:2IMJFPAR submitted 2023-07-07 cs.CR

Naoise Holohan This is my paper · ORCID
classification cs.CR
keywords seedinggeneratorsnumberprivacyrandomallowdifferentialprngs
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Differential Privacy (DP) relies on random numbers to preserve privacy, typically utilising Pseudorandom Number Generators (PRNGs) as a source of randomness. In order to allow for consistent reproducibility, testing and bug-fixing in DP algorithms and results, it is important to allow for the seeding of the PRNGs used therein. In this work, we examine the landscape of Random Number Generators (RNGs), and the considerations software engineers should make when choosing and seeding a PRNG for DP. We hope it serves as a suitable guide for DP practitioners, and includes many lessons learned when implementing seeding for diffprivlib.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Energy-Efficient Sampling Using Stochastic Magnetic Tunnel Junctions

    physics.comp-ph 2024-12 conditional novelty 6.0 of 10

    A bit-by-bit configuration of stochastic magnetic tunnel junctions produces uniform Float16 samples and enables low-energy sampling from arbitrary 1D distributions.

  2. Entropy Mixing Networks: Enhancing Pseudo-Random Number Generators with Lightweight Dynamic Entropy Injection

    cs.CR 2025-01 reject novelty 2.0 of 10

    A hybrid generator that hashes OS entropy into a Mersenne Twister reports higher chi-squared p-value and entropy than two baselines, but the differences are tiny and the runs test numbers are inconsistent.

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