pub struct JitterRng { /* private fields */ }
Expand description
A true random number generator based on jitter in the CPU execution time, and jitter in memory access time.
Implementations
sourceimpl JitterRng
impl JitterRng
sourcepub fn new() -> Result<JitterRng, TimerError>
pub fn new() -> Result<JitterRng, TimerError>
Create a new JitterRng
. Makes use of std::time
for a timer, or a
platform-specific function with higher accuracy if necessary and
available.
During initialization CPU execution timing jitter is measured a few hundred times. If this does not pass basic quality tests, an error is returned. The test result is cached to make subsequent calls faster.
sourcepub fn new_with_timer(timer: fn() -> u64) -> JitterRng
pub fn new_with_timer(timer: fn() -> u64) -> JitterRng
Create a new JitterRng
.
A custom timer can be supplied, making it possible to use JitterRng
in
no_std
environments.
The timer must have nanosecond precision.
This method is more low-level than new()
. It is the responsibility of
the caller to run test_timer
before using any numbers generated with
JitterRng
, and optionally call set_rounds
. Also it is important to
consume at least one u64
before using the first result to initialize
the entropy collection pool.
Example
use rand_jitter::JitterRng;
fn get_nstime() -> u64 {
use std::time::{SystemTime, UNIX_EPOCH};
let dur = SystemTime::now().duration_since(UNIX_EPOCH).unwrap();
// The correct way to calculate the current time is
// `dur.as_secs() * 1_000_000_000 + dur.subsec_nanos() as u64`
// But this is faster, and the difference in terms of entropy is
// negligible (log2(10^9) == 29.9).
dur.as_secs() << 30 | dur.subsec_nanos() as u64
}
let mut rng = JitterRng::new_with_timer(get_nstime);
let rounds = rng.test_timer()?;
rng.set_rounds(rounds); // optional
let _ = rng.next_u64();
// Ready for use
let v: u64 = rng.next_u64();
sourcepub fn set_rounds(&mut self, rounds: u8)
pub fn set_rounds(&mut self, rounds: u8)
Configures how many rounds are used to generate each 64-bit value. This must be greater than zero, and has a big impact on performance and output quality.
new_with_timer
conservatively uses 64 rounds, but often less rounds
can be used. The test_timer()
function returns the minimum number of
rounds required for full strength (platform dependent), so one may use
rng.set_rounds(rng.test_timer()?);
or cache the value.
sourcepub fn test_timer(&mut self) -> Result<u8, TimerError>
pub fn test_timer(&mut self) -> Result<u8, TimerError>
Basic quality tests on the timer, by measuring CPU timing jitter a few hundred times.
If successful, this will return the estimated number of rounds necessary
to collect 64 bits of entropy. Otherwise a TimerError
with the cause
of the failure will be returned.
sourcepub fn timer_stats(&mut self, var_rounds: bool) -> i64
pub fn timer_stats(&mut self, var_rounds: bool) -> i64
Statistical test: return the timer delta of one normal run of the
JitterRng
entropy collector.
Setting var_rounds
to true
will execute the memory access and the
CPU jitter noice sources a variable amount of times (just like a real
JitterRng
round).
Setting var_rounds
to false
will execute the noice sources the
minimal number of times. This can be used to measure the minimum amount
of entropy one round of the entropy collector can collect in the worst
case.
See this crate’s README on how to use timer_stats
to test the quality
of JitterRng
.
Trait Implementations
impl CryptoRng for JitterRng
Auto Trait Implementations
impl RefUnwindSafe for JitterRng
impl Send for JitterRng
impl Sync for JitterRng
impl Unpin for JitterRng
impl UnwindSafe for JitterRng
Blanket Implementations
sourceimpl<T> BorrowMut<T> for T where
T: ?Sized,
impl<T> BorrowMut<T> for T where
T: ?Sized,
const: unstable · sourcepub fn borrow_mut(&mut self) -> &mut T
pub fn borrow_mut(&mut self) -> &mut T
Mutably borrows from an owned value. Read more
sourceimpl<R> Rng for R where
R: RngCore + ?Sized,
impl<R> Rng for R where
R: RngCore + ?Sized,
sourcefn gen<T>(&mut self) -> T where
Standard: Distribution<T>,
fn gen<T>(&mut self) -> T where
Standard: Distribution<T>,
sourcefn gen_range<T: SampleUniform, B1, B2>(&mut self, low: B1, high: B2) -> T where
B1: SampleBorrow<T> + Sized,
B2: SampleBorrow<T> + Sized,
fn gen_range<T: SampleUniform, B1, B2>(&mut self, low: B1, high: B2) -> T where
B1: SampleBorrow<T> + Sized,
B2: SampleBorrow<T> + Sized,
Generate a random value in the range [low
, high
), i.e. inclusive of
low
and exclusive of high
. Read more
sourcefn sample<T, D: Distribution<T>>(&mut self, distr: D) -> T
fn sample<T, D: Distribution<T>>(&mut self, distr: D) -> T
Sample a new value, using the given distribution. Read more
sourcefn sample_iter<'a, T, D: Distribution<T>>(
&'a mut self,
distr: &'a D
) -> DistIter<'a, D, Self, T>ⓘNotable traits for DistIter<'a, D, R, T>impl<'a, D, R, T> Iterator for DistIter<'a, D, R, T> where
D: Distribution<T>,
R: Rng + 'a, type Item = T;
where
Self: Sized,
fn sample_iter<'a, T, D: Distribution<T>>(
&'a mut self,
distr: &'a D
) -> DistIter<'a, D, Self, T>ⓘNotable traits for DistIter<'a, D, R, T>impl<'a, D, R, T> Iterator for DistIter<'a, D, R, T> where
D: Distribution<T>,
R: Rng + 'a, type Item = T;
where
Self: Sized,
D: Distribution<T>,
R: Rng + 'a, type Item = T;
Create an iterator that generates values using the given distribution. Read more
sourcefn fill<T: AsByteSliceMut + ?Sized>(&mut self, dest: &mut T)
fn fill<T: AsByteSliceMut + ?Sized>(&mut self, dest: &mut T)
Fill dest
entirely with random bytes (uniform value distribution),
where dest
is any type supporting AsByteSliceMut
, namely slices
and arrays over primitive integer types (i8
, i16
, u32
, etc.). Read more
sourcefn try_fill<T: AsByteSliceMut + ?Sized>(
&mut self,
dest: &mut T
) -> Result<(), Error>
fn try_fill<T: AsByteSliceMut + ?Sized>(
&mut self,
dest: &mut T
) -> Result<(), Error>
Fill dest
entirely with random bytes (uniform value distribution),
where dest
is any type supporting AsByteSliceMut
, namely slices
and arrays over primitive integer types (i8
, i16
, u32
, etc.). Read more
sourcefn gen_bool(&mut self, p: f64) -> bool
fn gen_bool(&mut self, p: f64) -> bool
Return a bool with a probability p
of being true. Read more
sourcefn gen_ratio(&mut self, numerator: u32, denominator: u32) -> bool
fn gen_ratio(&mut self, numerator: u32, denominator: u32) -> bool
Return a bool with a probability of numerator/denominator
of being
true. I.e. gen_ratio(2, 3)
has chance of 2 in 3, or about 67%, of
returning true. If numerator == denominator
, then the returned value
is guaranteed to be true
. If numerator == 0
, then the returned
value is guaranteed to be false
. Read more
sourcefn choose<'a, T>(&mut self, values: &'a [T]) -> Option<&'a T>
fn choose<'a, T>(&mut self, values: &'a [T]) -> Option<&'a T>
use SliceRandom::choose instead
Return a random element from values
. Read more
sourcefn choose_mut<'a, T>(&mut self, values: &'a mut [T]) -> Option<&'a mut T>
fn choose_mut<'a, T>(&mut self, values: &'a mut [T]) -> Option<&'a mut T>
use SliceRandom::choose_mut instead
Return a mutable pointer to a random element from values
. Read more
sourceimpl<T> ToOwned for T where
T: Clone,
impl<T> ToOwned for T where
T: Clone,
type Owned = T
type Owned = T
The resulting type after obtaining ownership.
sourcepub fn to_owned(&self) -> T
pub fn to_owned(&self) -> T
Creates owned data from borrowed data, usually by cloning. Read more
sourcepub fn clone_into(&self, target: &mut T)
pub fn clone_into(&self, target: &mut T)
toowned_clone_into
)Uses borrowed data to replace owned data, usually by cloning. Read more