Random number generators and adapters
Computers cannot produce random numbers from nowhere. We classify random number generators as follows:
- “True” random number generators (TRNGs) use hard-to-predict data sources (e.g. the high-resolution parts of event timings and sensor jitter) to harvest random bit-sequences, apply algorithms to remove bias and estimate available entropy, then combine these bits into a byte-sequence or an entropy pool. This job is usually done by the operating system or a hardware generator (HRNG).
- “Pseudo”-random number generators (PRNGs) use algorithms to transform a seed into a sequence of pseudo-random numbers. These generators can be fast and produce well-distributed unpredictable random numbers (or not). They are usually deterministic: given algorithm and seed, the output sequence can be reproduced. They have finite period and eventually loop; with many algorithms this period is fixed and can be proven sufficiently long, while others are chaotic and the period depends on the seed.
- “Cryptographically secure” pseudo-random number generators (CSPRNGs) are the sub-set of PRNGs which are secure. Security of the generator relies both on hiding the internal state and using a strong algorithm.
All PRNGs require a seed to produce their random number sequence. The
SeedableRng trait provides three ways of constructing PRNGs:
from_seedaccepts a type specific to the PRNG
from_rngallows a PRNG to be seeded from any other RNG
seed_from_u64allows any PRNG to be seeded from a
from_entropysecurely seeds a PRNG from fresh entropy
rand_core crate when implementing your own RNGs.
This crate provides several random number generators:
OsRngis an interface to the operating system’s random number source. Typically the operating system uses a CSPRNG with entropy provided by a TRNG and some type of on-going re-seeding.
ThreadRng], provided by the
thread_rngfunction, is a handle to a thread-local CSPRNG with periodic seeding from
OsRng. Because this is local, it is typically much faster than
OsRng. It should be secure, though the paranoid may prefer
StdRng] is a CSPRNG chosen for good performance and trust of security (based on reviews, maturity and usage). The current algorithm is ChaCha12, which is well established and rigorously analysed. [
StdRng] provides the algorithm used by [
ThreadRng] but without periodic reseeding.
SmallRng] is an insecure PRNG designed to be fast, simple, require little memory, and have good output quality.
The algorithms selected for [
StdRng] and [
SmallRng] may change in any
release and may be platform-dependent, therefore they should be considered
rdrand crate provides an interface to the RDRAND and
RDSEED instructions available in modern Intel and AMD CPUs.
rand_jitter crate provides a user-space implementation of
entropy harvesting from CPU timer jitter, but is very slow and has
PRNGs: Several companion crates are available, providing individual or
families of PRNG algorithms. These provide the implementations behind
StdRng] and [
SmallRng] but can also be used directly, indeed should
be used directly when reproducibility matters.
Some suggestions are:
A full list can be found by searching for crates with the