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Migrate group to rand 0.5

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This commit is contained in:
Jack Grigg
2019-07-10 18:35:57 -04:00
parent 7a6642b221
commit ce6e2a5825
4 changed files with 104 additions and 55 deletions
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2
group/Cargo.toml
View File

@@ -14,4 +14,4 @@ repository = "https://github.com/ebfull/group"
[dependencies]
ff = { path = "../ff" }
rand = "0.4"
rand = "0.5"

16
group/src/lib.rs
View File

@@ -2,6 +2,7 @@ extern crate ff;
extern crate rand;
use ff::{PrimeField, PrimeFieldDecodingError, ScalarEngine, SqrtField};
use rand::RngCore;
use std::error::Error;
use std::fmt;
@@ -13,23 +14,16 @@ pub use self::wnaf::Wnaf;
/// Projective representation of an elliptic curve point guaranteed to be
/// in the correct prime order subgroup.
pub trait CurveProjective:
PartialEq
+ Eq
+ Sized
+ Copy
+ Clone
+ Send
+ Sync
+ fmt::Debug
+ fmt::Display
+ rand::Rand
+ 'static
PartialEq + Eq + Sized + Copy + Clone + Send + Sync + fmt::Debug + fmt::Display + 'static
{
type Engine: ScalarEngine<Fr = Self::Scalar>;
type Scalar: PrimeField + SqrtField;
type Base: SqrtField;
type Affine: CurveAffine<Projective = Self, Scalar = Self::Scalar>;
/// Returns an element chosen uniformly at random using a user-provided RNG.
fn random<R: RngCore>(rng: &mut R) -> Self;
/// Returns the additive identity.
fn zero() -> Self;

111
group/src/tests/mod.rs
View File

@@ -1,9 +1,13 @@
use rand::{Rand, Rng, SeedableRng, XorShiftRng};
use ff::{Field, PrimeField};
use rand::{SeedableRng, XorShiftRng};
use {CurveAffine, CurveProjective, EncodedPoint};
pub fn curve_tests<G: CurveProjective>() {
let mut rng = XorShiftRng::from_seed([0x5dbe6259, 0x8d313d76, 0x3237db17, 0xe5bc0654]);
let mut rng = XorShiftRng::from_seed([
0x59, 0x62, 0xbe, 0x5d, 0x76, 0x3d, 0x31, 0x8d, 0x17, 0xdb, 0x37, 0x32, 0x54, 0x06, 0xbc,
0xe5,
]);
// Negation edge case with zero.
{
@@ -21,7 +25,7 @@ pub fn curve_tests<G: CurveProjective>() {
// Addition edge cases with zero
{
let mut r = G::rand(&mut rng);
let mut r = G::random(&mut rng);
let rcopy = r;
r.add_assign(&G::zero());
assert_eq!(r, rcopy);
@@ -45,9 +49,10 @@ pub fn curve_tests<G: CurveProjective>() {
// Transformations
{
let a = G::rand(&mut rng);
let a = G::random(&mut rng);
let b = a.into_affine().into_projective();
let c = a.into_affine()
let c = a
.into_affine()
.into_projective()
.into_affine()
.into_projective();
@@ -65,11 +70,12 @@ pub fn curve_tests<G: CurveProjective>() {
}
fn random_wnaf_tests<G: CurveProjective>() {
use ff::PrimeField;
use wnaf::*;
let mut rng = XorShiftRng::from_seed([0x5dbe6259, 0x8d313d76, 0x3237db17, 0xe5bc0654]);
let mut rng = XorShiftRng::from_seed([
0x59, 0x62, 0xbe, 0x5d, 0x76, 0x3d, 0x31, 0x8d, 0x17, 0xdb, 0x37, 0x32, 0x54, 0x06, 0xbc,
0xe5,
]);
{
let mut table = vec![];
@@ -77,8 +83,8 @@ fn random_wnaf_tests<G: CurveProjective>() {
for w in 2..14 {
for _ in 0..100 {
let g = G::rand(&mut rng);
let s = G::Scalar::rand(&mut rng).into_repr();
let g = G::random(&mut rng);
let s = G::Scalar::random(&mut rng).into_repr();
let mut g1 = g;
g1.mul_assign(s);
@@ -95,8 +101,8 @@ fn random_wnaf_tests<G: CurveProjective>() {
fn only_compiles_if_send<S: Send>(_: &S) {}
for _ in 0..100 {
let g = G::rand(&mut rng);
let s = G::Scalar::rand(&mut rng).into_repr();
let g = G::random(&mut rng);
let s = G::Scalar::random(&mut rng).into_repr();
let mut g1 = g;
g1.mul_assign(s);
@@ -129,7 +135,8 @@ fn random_wnaf_tests<G: CurveProjective>() {
let mut wnaf = Wnaf::new();
{
// Populate the vectors.
wnaf.base(rng.gen(), 1).scalar(rng.gen());
wnaf.base(G::random(&mut rng), 1)
.scalar(G::Scalar::random(&mut rng).into_repr());
}
wnaf.base(g, 1).scalar(s)
};
@@ -137,7 +144,8 @@ fn random_wnaf_tests<G: CurveProjective>() {
let mut wnaf = Wnaf::new();
{
// Populate the vectors.
wnaf.base(rng.gen(), 1).scalar(rng.gen());
wnaf.base(G::random(&mut rng), 1)
.scalar(G::Scalar::random(&mut rng).into_repr());
}
wnaf.scalar(s).base(g)
};
@@ -145,7 +153,8 @@ fn random_wnaf_tests<G: CurveProjective>() {
let mut wnaf = Wnaf::new();
{
// Populate the vectors.
wnaf.base(rng.gen(), 1).scalar(rng.gen());
wnaf.base(G::random(&mut rng), 1)
.scalar(G::Scalar::random(&mut rng).into_repr());
}
let mut shared = wnaf.base(g, 1).shared();
@@ -157,7 +166,8 @@ fn random_wnaf_tests<G: CurveProjective>() {
let mut wnaf = Wnaf::new();
{
// Populate the vectors.
wnaf.base(rng.gen(), 1).scalar(rng.gen());
wnaf.base(G::random(&mut rng), 1)
.scalar(G::Scalar::random(&mut rng).into_repr());
}
let mut shared = wnaf.scalar(s).shared();
@@ -179,14 +189,15 @@ fn random_wnaf_tests<G: CurveProjective>() {
}
fn random_negation_tests<G: CurveProjective>() {
use ff::Field;
let mut rng = XorShiftRng::from_seed([0x5dbe6259, 0x8d313d76, 0x3237db17, 0xe5bc0654]);
let mut rng = XorShiftRng::from_seed([
0x59, 0x62, 0xbe, 0x5d, 0x76, 0x3d, 0x31, 0x8d, 0x17, 0xdb, 0x37, 0x32, 0x54, 0x06, 0xbc,
0xe5,
]);
for _ in 0..1000 {
let r = G::rand(&mut rng);
let r = G::random(&mut rng);
let s = G::Scalar::rand(&mut rng);
let s = G::Scalar::random(&mut rng);
let mut sneg = s;
sneg.negate();
@@ -210,11 +221,14 @@ fn random_negation_tests<G: CurveProjective>() {
}
fn random_doubling_tests<G: CurveProjective>() {
let mut rng = XorShiftRng::from_seed([0x5dbe6259, 0x8d313d76, 0x3237db17, 0xe5bc0654]);
let mut rng = XorShiftRng::from_seed([
0x59, 0x62, 0xbe, 0x5d, 0x76, 0x3d, 0x31, 0x8d, 0x17, 0xdb, 0x37, 0x32, 0x54, 0x06, 0xbc,
0xe5,
]);
for _ in 0..1000 {
let mut a = G::rand(&mut rng);
let mut b = G::rand(&mut rng);
let mut a = G::random(&mut rng);
let mut b = G::random(&mut rng);
// 2(a + b)
let mut tmp1 = a;
@@ -237,15 +251,18 @@ fn random_doubling_tests<G: CurveProjective>() {
}
fn random_multiplication_tests<G: CurveProjective>() {
let mut rng = XorShiftRng::from_seed([0x5dbe6259, 0x8d313d76, 0x3237db17, 0xe5bc0654]);
let mut rng = XorShiftRng::from_seed([
0x59, 0x62, 0xbe, 0x5d, 0x76, 0x3d, 0x31, 0x8d, 0x17, 0xdb, 0x37, 0x32, 0x54, 0x06, 0xbc,
0xe5,
]);
for _ in 0..1000 {
let mut a = G::rand(&mut rng);
let mut b = G::rand(&mut rng);
let mut a = G::random(&mut rng);
let mut b = G::random(&mut rng);
let a_affine = a.into_affine();
let b_affine = b.into_affine();
let s = G::Scalar::rand(&mut rng);
let s = G::Scalar::random(&mut rng);
// s ( a + b )
let mut tmp1 = a;
@@ -269,12 +286,15 @@ fn random_multiplication_tests<G: CurveProjective>() {
}
fn random_addition_tests<G: CurveProjective>() {
let mut rng = XorShiftRng::from_seed([0x5dbe6259, 0x8d313d76, 0x3237db17, 0xe5bc0654]);
let mut rng = XorShiftRng::from_seed([
0x59, 0x62, 0xbe, 0x5d, 0x76, 0x3d, 0x31, 0x8d, 0x17, 0xdb, 0x37, 0x32, 0x54, 0x06, 0xbc,
0xe5,
]);
for _ in 0..1000 {
let a = G::rand(&mut rng);
let b = G::rand(&mut rng);
let c = G::rand(&mut rng);
let a = G::random(&mut rng);
let b = G::random(&mut rng);
let c = G::random(&mut rng);
let a_affine = a.into_affine();
let b_affine = b.into_affine();
let c_affine = c.into_affine();
@@ -347,10 +367,13 @@ fn random_addition_tests<G: CurveProjective>() {
}
fn random_transformation_tests<G: CurveProjective>() {
let mut rng = XorShiftRng::from_seed([0x5dbe6259, 0x8d313d76, 0x3237db17, 0xe5bc0654]);
let mut rng = XorShiftRng::from_seed([
0x59, 0x62, 0xbe, 0x5d, 0x76, 0x3d, 0x31, 0x8d, 0x17, 0xdb, 0x37, 0x32, 0x54, 0x06, 0xbc,
0xe5,
]);
for _ in 0..1000 {
let g = G::rand(&mut rng);
let g = G::random(&mut rng);
let g_affine = g.into_affine();
let g_projective = g_affine.into_projective();
assert_eq!(g, g_projective);
@@ -358,24 +381,25 @@ fn random_transformation_tests<G: CurveProjective>() {
// Batch normalization
for _ in 0..10 {
let mut v = (0..1000).map(|_| G::rand(&mut rng)).collect::<Vec<_>>();
let mut v = (0..1000).map(|_| G::random(&mut rng)).collect::<Vec<_>>();
for i in &v {
assert!(!i.is_normalized());
}
use rand::distributions::{IndependentSample, Range};
let between = Range::new(0, 1000);
use rand::distributions::{Distribution, Uniform};
let between = Uniform::new(0, 1000);
// Sprinkle in some normalized points
for _ in 0..5 {
v[between.ind_sample(&mut rng)] = G::zero();
v[between.sample(&mut rng)] = G::zero();
}
for _ in 0..5 {
let s = between.ind_sample(&mut rng);
let s = between.sample(&mut rng);
v[s] = v[s].into_affine().into_projective();
}
let expected_v = v.iter()
let expected_v = v
.iter()
.map(|v| v.into_affine().into_projective())
.collect::<Vec<_>>();
G::batch_normalization(&mut v);
@@ -389,7 +413,10 @@ fn random_transformation_tests<G: CurveProjective>() {
}
fn random_encoding_tests<G: CurveAffine>() {
let mut rng = XorShiftRng::from_seed([0x5dbe6259, 0x8d313d76, 0x3237db17, 0xe5bc0654]);
let mut rng = XorShiftRng::from_seed([
0x59, 0x62, 0xbe, 0x5d, 0x76, 0x3d, 0x31, 0x8d, 0x17, 0xdb, 0x37, 0x32, 0x54, 0x06, 0xbc,
0xe5,
]);
assert_eq!(
G::zero().into_uncompressed().into_affine().unwrap(),
@@ -402,7 +429,7 @@ fn random_encoding_tests<G: CurveAffine>() {
);
for _ in 0..1000 {
let mut r = G::Projective::rand(&mut rng).into_affine();
let mut r = G::Projective::random(&mut rng).into_affine();
let uncompressed = r.into_uncompressed();
let de_uncompressed = uncompressed.into_affine().unwrap();