Random Number Generators and Seeding for Differential Privacy

TL;DR

This paper reviews the role of random number generators and seeding in differential privacy, providing guidance for practitioners to ensure reproducibility and security in DP algorithms.

Contribution

It offers a comprehensive analysis of RNG options and seeding practices tailored for differential privacy applications, including lessons from implementation experience.

Findings

Guidelines for selecting RNGs in DP

Best practices for seeding PRNGs in DP

Insights from diffprivlib implementation

Abstract

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.