Module rand::rngs

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Expand description

Random number generators and adapters

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Background: Random number generators (RNGs)

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Computers cannot produce random numbers from nowhere. We classify -random number generators as follows:

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“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.

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Traits and functionality

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All RNGs implement the RngCore trait, as a consequence of which the -Rng extension trait is automatically implemented. Secure RNGs may -additionally implement the CryptoRng trait.

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All PRNGs require a seed to produce their random number sequence. The -SeedableRng trait provides three ways of constructing PRNGs:

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from_seed accepts a type specific to the PRNG
from_rng allows a PRNG to be seeded from any other RNG
seed_from_u64 allows any PRNG to be seeded from a u64 insecurely
from_entropy securely seeds a PRNG from fresh entropy

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Use the rand_core crate when implementing your own RNGs.

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Our generators

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This crate provides several random number generators:

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OsRng is 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_rng function, 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 OsRng.
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.

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The algorithms selected for StdRng and [SmallRng] may change in any -release and may be platform-dependent, therefore they should be considered -not reproducible.

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TRNGs: The rdrand crate provides an interface to the RDRAND and -RDSEED instructions available in modern Intel and AMD CPUs. -The rand_jitter crate provides a user-space implementation of -entropy harvesting from CPU timer jitter, but is very slow and has -security issues.

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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: rand_chacha, rand_pcg, rand_xoshiro. -A full list can be found by searching for crates with the rng tag.

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Modules

adapter

Wrappers / adapters forming RNGs

mock

Mock random number generator

Structs

OsRng

A random number generator that retrieves randomness from the -operating system.

StdRng

The standard RNG. The PRNG algorithm in StdRng is chosen to be efficient -on the current platform, to be statistically strong and unpredictable -(meaning a cryptographically secure PRNG).

ThreadRng