Files
rosenpass/constant-time/src/memcmp.rs

122 lines
4.2 KiB
Rust

//! memcmp
/// compares two sclices of memory content and returns whether they are equal
///
/// ## Leaks
/// If the two slices have differents lengths, the function will return immediately. This
/// effectively leaks the information whether the slices have equal length or not. This is widely
/// considered safe.
///
/// The execution time of the function grows approx. linear with the length of the input. This is
/// considered safe.
///
/// ## Examples
///
/// ```rust
/// use rosenpass_constant_time::memcmp;
/// let a = [0, 0, 0, 0];
/// let b = [0, 0, 0, 1];
/// let c = [0, 0, 0];
/// assert!(memcmp(&a, &a));
/// assert!(!memcmp(&a, &b));
/// assert!(!memcmp(&a, &c));
/// ```
#[inline]
pub fn memcmp(a: &[u8], b: &[u8]) -> bool {
a.len() == b.len() && unsafe { memsec::memeq(a.as_ptr(), b.as_ptr(), a.len()) }
}
/// [tests::memcmp_runs_in_constant_time] runs a stasticial test that the equality of the two
/// input parameters does not correlate with the run time.
///
/// For discussion on how to (further) ensure the constant-time execution of this function,
/// see <https://github.com/rosenpass/rosenpass/issues/232>
#[cfg(all(test, feature = "constant_time_tests"))]
mod tests {
use super::*;
use rand::seq::SliceRandom;
use rand::thread_rng;
use std::time::Instant;
#[test]
/// tests whether [memcmp] actually runs in constant time
///
/// This test function will run an equal amount of comparisons on two different sets of parameters:
/// - completely equal slices
/// - completely unequal slices.
/// All comparisons are executed in a randomized order. The test will fail if one of the
/// two sets is checked for equality significantly faster than the other set
/// (absolute correlation coefficient ≥ 0.01)
fn memcmp_runs_in_constant_time() {
// prepare data to compare
let n: usize = 1E6 as usize; // number of comparisons to run
let len = 1024; // length of each slice passed as parameters to the tested comparison function
let a1 = "a".repeat(len);
let a2 = a1.clone();
let b = "b".repeat(len);
let a1 = a1.as_bytes();
let a2 = a2.as_bytes();
let b = b.as_bytes();
// vector representing all timing tests
//
// Each element is a tuple of:
// 0: whether the test compared two equal slices
// 1: the duration needed for the comparison to run
let mut tests = (0..n)
.map(|i| (i < n / 2, std::time::Duration::ZERO))
.collect::<Vec<_>>();
tests.shuffle(&mut thread_rng());
// run comparisons / call function to test
for test in tests.iter_mut() {
let now = Instant::now();
if test.0 {
memcmp(a1, a2);
} else {
memcmp(a1, b);
}
test.1 = now.elapsed();
// println!("eq: {}, elapsed: {:.2?}", test.0, test.1);
}
// sort by execution time and calculate Pearson correlation coefficient
tests.sort_by_key(|v| v.1);
let tests = tests
.iter()
.map(|t| (if t.0 { 1_f64 } else { 0_f64 }, t.1.as_nanos() as f64))
.collect::<Vec<_>>();
// averages
let (avg_x, avg_y): (f64, f64) = (
tests.iter().map(|t| t.0).sum::<f64>() / n as f64,
tests.iter().map(|t| t.1).sum::<f64>() / n as f64,
);
assert!((avg_x - 0.5).abs() < 1E-12);
// standard deviations
let sd_x = 0.5;
let sd_y = (1_f64 / n as f64
* tests
.iter()
.map(|t| {
let difference = t.1 - avg_y;
difference * difference
})
.sum::<f64>())
.sqrt();
// covariance
let cv = 1_f64 / n as f64
* tests
.iter()
.map(|t| (t.0 - avg_x) * (t.1 - avg_y))
.sum::<f64>();
// Pearson correlation
let correlation = cv / (sd_x * sd_y);
println!("correlation: {:.6?}", correlation);
assert!(
correlation.abs() < 0.01,
"execution time correlates with result"
)
}
}