# Random Number Generators and Seeding for Differential Privacy

arXiv: https://arxiv.org/abs/2307.03543 · HTML: https://mlsanity.com/paper/2307.03543/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jul 2023.

## Built on by (most cited first, 1 of 1)

- [One RNG to Rule Them All: How Randomness Becomes an Attack Vector in Machine Learning](https://mlsanity.com/paper/2602.09182/) (2026)

## Abstract (arXiv)

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.