Module rngs

Random number generators and adapters

Background: Random number generators (RNGs)

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.

Traits and functionality

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.

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

• 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

Use the rand_core crate when implementing your own RNGs.

Our generators

This crate provides several random number generators:

• 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.

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

Additional generators

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.

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.

Modules

• adapterstd
  Wrappers / adapters forming RNGs
• mock
  Mock random number generator

Structs

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