arnaucube/miden-crypto
1
0
Fork 0
mirror of https://github.com/arnaucube/miden-crypto.git synced 2026-01-13 01:21:29 +01:00
Code Issues Projects Releases Wiki Activity

Tracking PR for v0.9.0 release (#278)

Browse Source 
* chore: update crate version to v0.9.0
* chore: remove deprecated re-exports
* chore: remove Box re-export
* feat: implement pure-Rust keygen and signing for RpoFalcon512 (#285)
* feat: add reproducible builds (#296)
* fix: address a few issues for migrating Miden VM  (#298)
* feat: add RngCore supertrait for FeltRng (#299)

---------

Co-authored-by: Al-Kindi-0 <82364884+Al-Kindi-0@users.noreply.github.com>
Co-authored-by: Paul-Henry Kajfasz <42912740+phklive@users.noreply.github.com>
This commit is contained in:
Bobbin Threadbare
2024-03-24 08:38:08 -07:00
committed by GitHub
parent 2be17b74fb
commit 5a2e917dd5
39 changed files with 5964 additions and 2334 deletions
Download Patch File Download Diff File Expand all files Collapse all files

298
src/dsa/rpo_falcon512/math/samplerz.rs Normal file
View File

@@ -0,0 +1,298 @@
use core::f64::consts::LN_2;
use rand::Rng;
#[cfg(not(feature = "std"))]
use num::Float;
/// Samples an integer from {0, ..., 18} according to the distribution χ, which is close to
/// the half-Gaussian distribution on the natural numbers with mean 0 and standard deviation
/// equal to sigma_max.
fn base_sampler(bytes: [u8; 9]) -> i16 {
const RCDT: [u128; 18] = [
3024686241123004913666,
1564742784480091954050,
636254429462080897535,
199560484645026482916,
47667343854657281903,
8595902006365044063,
1163297957344668388,
117656387352093658,
8867391802663976,
496969357462633,
20680885154299,
638331848991,
14602316184,
247426747,
3104126,
28824,
198,
1,
];
let u = u128::from_be_bytes([vec![0u8; 7], bytes.to_vec()].concat().try_into().unwrap());
RCDT.into_iter().filter(|r| u < *r).count() as i16
}
/// Computes an integer approximation of 2^63 * ccs * exp(-x).
fn approx_exp(x: f64, ccs: f64) -> u64 {
// The constants C are used to approximate exp(-x); these
// constants are taken from FACCT (up to a scaling factor
// of 2^63):
// https://eprint.iacr.org/2018/1234
// https://github.com/raykzhao/gaussian
const C: [u64; 13] = [
0x00000004741183A3u64,
0x00000036548CFC06u64,
0x0000024FDCBF140Au64,
0x0000171D939DE045u64,
0x0000D00CF58F6F84u64,
0x000680681CF796E3u64,
0x002D82D8305B0FEAu64,
0x011111110E066FD0u64,
0x0555555555070F00u64,
0x155555555581FF00u64,
0x400000000002B400u64,
0x7FFFFFFFFFFF4800u64,
0x8000000000000000u64,
];
let mut z: u64;
let mut y: u64;
let twoe63 = 1u64 << 63;
y = C[0];
z = f64::floor(x * (twoe63 as f64)) as u64;
for cu in C.iter().skip(1) {
let zy = (z as u128) * (y as u128);
y = cu - ((zy >> 63) as u64);
}
z = f64::floor((twoe63 as f64) * ccs) as u64;
(((z as u128) * (y as u128)) >> 63) as u64
}
/// A random bool that is true with probability ≈ ccs · exp(-x).
fn ber_exp(x: f64, ccs: f64, random_bytes: [u8; 7]) -> bool {
// 0.69314718055994530941 = ln(2)
let s = f64::floor(x / LN_2) as usize;
let r = x - LN_2 * (s as f64);
let shamt = usize::min(s, 63);
let z = ((((approx_exp(r, ccs) as u128) << 1) - 1) >> shamt) as u64;
let mut w = 0i16;
for (index, i) in (0..64).step_by(8).rev().enumerate() {
let byte = random_bytes[index];
w = (byte as i16) - (((z >> i) & 0xff) as i16);
if w != 0 {
break;
}
}
w < 0
}
/// Samples an integer from the Gaussian distribution with given mean (mu) and standard deviation
/// (sigma).
pub(crate) fn sampler_z<R: Rng>(mu: f64, sigma: f64, sigma_min: f64, rng: &mut R) -> i16 {
const SIGMA_MAX: f64 = 1.8205;
const INV_2SIGMA_MAX_SQ: f64 = 1f64 / (2f64 * SIGMA_MAX * SIGMA_MAX);
let isigma = 1f64 / sigma;
let dss = 0.5f64 * isigma * isigma;
let s = f64::floor(mu);
let r = mu - s;
let ccs = sigma_min * isigma;
loop {
let z0 = base_sampler(rng.gen());
let random_byte: u8 = rng.gen();
let b = (random_byte & 1) as i16;
let z = b + ((b << 1) - 1) * z0;
let zf_min_r = (z as f64) - r;
// x = ((z-r)^2)/(2*sigma^2) - ((z-b)^2)/(2*sigma0^2)
let x = zf_min_r * zf_min_r * dss - (z0 * z0) as f64 * INV_2SIGMA_MAX_SQ;
if ber_exp(x, ccs, rng.gen()) {
return z + (s as i16);
}
}
}
#[cfg(all(test, feature = "std"))]
mod test {
use alloc::vec::Vec;
use rand::RngCore;
use std::{thread::sleep, time::Duration};
use super::{approx_exp, ber_exp, sampler_z};
/// RNG used only for testing purposes, whereby the produced
/// string of random bytes is equal to the one it is initialized
/// with. Whatever you do, do not use this RNG in production.
struct UnsafeBufferRng {
buffer: Vec<u8>,
index: usize,
}
impl UnsafeBufferRng {
fn new(buffer: &[u8]) -> Self {
Self { buffer: buffer.to_vec(), index: 0 }
}
fn next(&mut self) -> u8 {
if self.buffer.len() <= self.index {
// panic!("Ran out of buffer.");
sleep(Duration::from_millis(10));
0u8
} else {
let return_value = self.buffer[self.index];
self.index += 1;
return_value
}
}
}
impl RngCore for UnsafeBufferRng {
fn next_u32(&mut self) -> u32 {
// let bytes: [u8; 4] = (0..4)
// .map(|_| self.next())
// .collect_vec()
// .try_into()
// .unwrap();
// u32::from_be_bytes(bytes)
u32::from_le_bytes([self.next(), 0, 0, 0])
}
fn next_u64(&mut self) -> u64 {
// let bytes: [u8; 8] = (0..8)
// .map(|_| self.next())
// .collect_vec()
// .try_into()
// .unwrap();
// u64::from_be_bytes(bytes)
u64::from_le_bytes([self.next(), 0, 0, 0, 0, 0, 0, 0])
}
fn fill_bytes(&mut self, dest: &mut [u8]) {
for d in dest.iter_mut() {
*d = self.next();
}
}
fn try_fill_bytes(&mut self, dest: &mut [u8]) -> Result<(), rand::Error> {
for d in dest.iter_mut() {
*d = self.next();
}
Ok(())
}
}
#[test]
fn test_unsafe_buffer_rng() {
let seed_bytes = hex::decode("7FFECD162AE2").unwrap();
let mut rng = UnsafeBufferRng::new(&seed_bytes);
let generated_bytes: Vec<u8> = (0..seed_bytes.len()).map(|_| rng.next()).collect();
assert_eq!(seed_bytes, generated_bytes);
}
#[test]
fn test_approx_exp() {
let precision = 1u64 << 14;
// known answers were generated with the following sage script:
//```sage
// num_samples = 10
// precision = 200
// R = Reals(precision)
//
// print(f"let kats : [(f64, f64, u64);{num_samples}] = [")
// for i in range(num_samples):
// x = RDF.random_element(0.0, 0.693147180559945)
// ccs = RDF.random_element(0.0, 1.0)
// res = round(2^63 * R(ccs) * exp(R(-x)))
// print(f"({x}, {ccs}, {res}),")
// print("];")
// ```
let kats: [(f64, f64, u64); 10] = [
(0.2314993926072656, 0.8148006314615972, 5962140072160879737),
(0.2648875572812225, 0.12769669655309035, 903712282351034505),
(0.11251957513682391, 0.9264611470305881, 7635725498677341553),
(0.04353439307256617, 0.5306497137523327, 4685877322232397936),
(0.41834495299784347, 0.879438856118578, 5338392138535350986),
(0.32579398973228557, 0.16513412873289002, 1099603299296456803),
(0.5939508073919817, 0.029776019144967303, 151637565622779016),
(0.2932367999399056, 0.37123847662857923, 2553827649386670452),
(0.5005699297417507, 0.31447208863888976, 1758235618083658825),
(0.4876437338498085, 0.6159515298936868, 3488632981903743976),
];
for (x, ccs, answer) in kats {
let difference = (answer as i128) - (approx_exp(x, ccs) as i128);
assert!(
(difference * difference) as u64 <= precision * precision,
"answer: {answer} versus approximation: {}\ndifference: {} whereas precision: {}",
approx_exp(x, ccs),
difference,
precision
);
}
}
#[test]
fn test_ber_exp() {
let kats = [
(
1.268_314_048_020_498_4,
0.749_990_853_267_664_9,
hex::decode("ea000000000000").unwrap(),
false,
),
(
0.001_563_917_959_143_409_6,
0.749_990_853_267_664_9,
hex::decode("6c000000000000").unwrap(),
true,
),
(
0.017_921_215_753_999_235,
0.749_990_853_267_664_9,
hex::decode("c2000000000000").unwrap(),
false,
),
(
0.776_117_648_844_980_6,
0.751_181_554_542_520_8,
hex::decode("58000000000000").unwrap(),
true,
),
];
for (x, ccs, bytes, answer) in kats {
assert_eq!(answer, ber_exp(x, ccs, bytes.try_into().unwrap()));
}
}
#[test]
fn test_sampler_z() {
let sigma_min = 1.277833697;
// known answers from the doc, table 3.2, page 44
// https://falcon-sign.info/falcon.pdf
// The zeros were added to account for dropped bytes.
let kats = [
(-91.90471153063714,1.7037990414754918,hex::decode("0fc5442ff043d66e91d1ea000000000000cac64ea5450a22941edc6c").unwrap(),-92),
(-8.322564895434937,1.7037990414754918,hex::decode("f4da0f8d8444d1a77265c2000000000000ef6f98bbbb4bee7db8d9b3").unwrap(),-8),
(-19.096516109216804,1.7035823083824078,hex::decode("db47f6d7fb9b19f25c36d6000000000000b9334d477a8bc0be68145d").unwrap(),-20),
(-11.335543982423326, 1.7035823083824078, hex::decode("ae41b4f5209665c74d00dc000000000000c1a8168a7bb516b3190cb42c1ded26cd52000000000000aed770eca7dd334e0547bcc3c163ce0b").unwrap(), -12),
(7.9386734193997555, 1.6984647769450156, hex::decode("31054166c1012780c603ae0000000000009b833cec73f2f41ca5807c000000000000c89c92158834632f9b1555").unwrap(), 8),
(-28.990850086867255, 1.6984647769450156, hex::decode("737e9d68a50a06dbbc6477").unwrap(), -30),
(-9.071257914091655, 1.6980782114808988, hex::decode("a98ddd14bf0bf22061d632").unwrap(), -10),
(-43.88754568839566, 1.6980782114808988, hex::decode("3cbf6818a68f7ab9991514").unwrap(), -41),
(-58.17435547946095,1.7010983419195522,hex::decode("6f8633f5bfa5d26848668e0000000000003d5ddd46958e97630410587c").unwrap(),-61),
(-43.58664906684732, 1.7010983419195522, hex::decode("272bc6c25f5c5ee53f83c40000000000003a361fbc7cc91dc783e20a").unwrap(), -46),
(-34.70565203313315, 1.7009387219711465, hex::decode("45443c59574c2c3b07e2e1000000000000d9071e6d133dbe32754b0a").unwrap(), -34),
(-44.36009577368896, 1.7009387219711465, hex::decode("6ac116ed60c258e2cbaeab000000000000728c4823e6da36e18d08da0000000000005d0cc104e21cc7fd1f5ca8000000000000d9dbb675266c928448059e").unwrap(), -44),
(-21.783037079346236, 1.6958406126012802, hex::decode("68163bc1e2cbf3e18e7426").unwrap(), -23),
(-39.68827784633828, 1.6958406126012802, hex::decode("d6a1b51d76222a705a0259").unwrap(), -40),
(-18.488607061056847, 1.6955259305261838, hex::decode("f0523bfaa8a394bf4ea5c10000000000000f842366fde286d6a30803").unwrap(), -22),
(-48.39610939101591, 1.6955259305261838, hex::decode("87bd87e63374cee62127fc0000000000006931104aab64f136a0485b").unwrap(), -50),
];
for (mu, sigma, random_bytes, answer) in kats {
assert_eq!(
sampler_z(mu, sigma, sigma_min, &mut UnsafeBufferRng::new(&random_bytes)),
answer
);
}
}
}