## Expand description

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

## Structs

`getrandom`

`small_rng`

`std_rng`

`StdRng`

is chosen to be efficient
on the current platform, to be statistically strong and unpredictable
(meaning a cryptographically secure PRNG).`std`

and `std_rng`