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Differential Privacy (DP) relies on random numbers to preserve privacy,typically utilising Pseudorandom Number Generators (PRNGs) as a source ofrandomness. In order to allow for consistent reproducibility, testing andbug-fixing in DP algorithms and results, it is important to allow for theseeding of the PRNGs used therein. In this work, we examine the landscape ofRandom Number Generators (RNGs), and the considerations software engineersshould make when choosing and seeding a PRNG for DP. We hope it serves as asuitable guide for DP practitioners, and includes many lessons learned whenimplementing seeding for diffprivlib.

Random Number Generators and Seeding for Differential Privacy