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use crate::{
share::{RabinShare, ShareVec},
Sharing,
};
use gf::{Field, GF};
pub struct RabinInformationDispersal {
n: u8,
k: u8,
}
impl RabinInformationDispersal {
pub fn new(n: u8, k: u8) -> Self {
Self { n, k }
}
}
impl Sharing for RabinInformationDispersal {
type Share = RabinShare;
fn share(&self, data: Vec<u8>) -> Option<Vec<Self::Share>> {
let length = data.len();
Some(
(1..=self.n)
.map(|x| {
let gx = GF(x);
RabinShare {
id: x,
length,
body: data
.chunks(self.k as usize)
.map(|chunk| {
chunk
.into_iter()
.rev()
.fold(GF::zero(), |res, b| GF(*b) + gx * res)
.into()
})
.collect(),
}
})
.collect(),
)
}
fn recontruct(&self, shares: Vec<Self::Share>) -> Option<Vec<u8>> {
if shares.len() < self.k as usize {
return None;
}
let xvalues = shares.iter().map(|x| x.id).collect();
let decoder = generate_decoder(self.k as usize, xvalues);
let mut secret = vec![0u8; shares.size()];
for i in 0..shares[0].body.len() {
for j in 0..self.k as usize {
let index = (i * self.k as usize) + j;
if index >= shares.size() { continue; }
secret[index] = (0..self.k as usize)
.map(|x| GF(decoder[j][x]) * GF(shares[x].body[i]))
.sum::<GF<u8>>()
.into();
}
}
Some(secret)
}
}
fn generate_decoder(size: usize, values: Vec<u8>) -> Vec<Vec<u8>> {
inverse(
(0..size)
.map(|i| (0..size).map(|j| GF(values[i]).pow(j).into()).collect())
.collect(),
)
}
fn two_mut<T>(sl: &mut [T], i: usize, j: usize) -> (&mut T, &mut T) {
let (smaller, lagger) = if i < j { (i, j) } else { (j, i) };
let (smsl, lgsl) = sl.split_at_mut(lagger);
if i == smaller {
(&mut smsl[smaller], &mut lgsl[0])
} else {
(&mut lgsl[0], &mut smsl[smaller])
}
}
fn inverse(matrix: Vec<Vec<u8>>) -> Vec<Vec<u8>> {
let size = matrix.len();
let mut res = generate_identity(size);
let mut tmp = matrix.clone();
for i in 0..size {
let inv = GF(tmp[i][i]).inverse().into();
normalize_row(&mut tmp[i][..], &mut res[i][..], inv);
for j in 0..size {
if j == i {
continue;
}
let coeff = tmp[j][i];
if coeff == 0 {
continue;
}
let (tmpi, tmpj) = two_mut(&mut tmp[..], i, j);
let (resi, resj) = two_mut(&mut res[..], i, j);
mult_and_subtract(&mut tmpj[..], &mut tmpi[..], coeff);
mult_and_subtract(&mut resj[..], &mut resi[..], coeff);
}
}
return res;
}
fn mult_and_subtract(row: &mut [u8], normalized: &[u8], coeff: u8) {
for i in 0..row.len() {
row[i] = (GF(row[i]) - GF(normalized[i]) * GF(coeff)).into();
}
}
fn normalize_row(tmp_row: &mut [u8], res_row: &mut [u8], element: u8) {
for i in 0..tmp_row.len() {
tmp_row[i] = (GF(tmp_row[i]) * GF(element)).into();
res_row[i] = (GF(res_row[i]) * GF(element)).into();
}
}
fn generate_identity(size: usize) -> Vec<Vec<u8>> {
(0..size)
.map(|i| (0..size).map(|j| if i == j { 1 } else { 0 }).collect())
.collect()
}