1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167

use ff::Field;
use rand_core::RngCore;

use super::{Params, ParamsIPA};
use crate::arithmetic::{
    best_multiexp, compute_inner_product, eval_polynomial, parallelize, CurveAffine, FieldExt,
};

use crate::poly::commitment::ParamsProver;
use crate::poly::{commitment::Blind, Coeff, Polynomial};
use crate::transcript::{EncodedChallenge, TranscriptWrite};

use group::Curve;
use std::io::{self, Write};

/// Create a polynomial commitment opening proof for the polynomial defined
/// by the coefficients `px`, the blinding factor `blind` used for the
/// polynomial commitment, and the point `x` that the polynomial is
/// evaluated at.
///
/// This function will panic if the provided polynomial is too large with
/// respect to the polynomial commitment parameters.
///
/// **Important:** This function assumes that the provided `transcript` has
/// already seen the common inputs: the polynomial commitment P, the claimed
/// opening v, and the point x. It's probably also nice for the transcript
/// to have seen the elliptic curve description and the URS, if you want to
/// be rigorous.
pub fn create_proof<
    C: CurveAffine,
    E: EncodedChallenge<C>,
    R: RngCore,
    T: TranscriptWrite<C, E>,
>(
    params: &ParamsIPA<C>,
    mut rng: R,
    transcript: &mut T,
    p_poly: &Polynomial<C::Scalar, Coeff>,
    p_blind: Blind<C::Scalar>,
    x_3: C::Scalar,
) -> io::Result<()> {
    // We're limited to polynomials of degree n - 1.
    assert_eq!(p_poly.len(), params.n as usize);

    // Sample a random polynomial (of same degree) that has a root at x_3, first
    // by setting all coefficients to random values.
    let mut s_poly = (*p_poly).clone();
    for coeff in s_poly.iter_mut() {
        *coeff = C::Scalar::random(&mut rng);
    }
    // Evaluate the random polynomial at x_3
    let s_at_x3 = eval_polynomial(&s_poly[..], x_3);
    // Subtract constant coefficient to get a random polynomial with a root at x_3
    s_poly[0] = s_poly[0] - &s_at_x3;
    // And sample a random blind
    let s_poly_blind = Blind(C::Scalar::random(&mut rng));

    // Write a commitment to the random polynomial to the transcript
    let s_poly_commitment = params.commit(&s_poly, s_poly_blind).to_affine();
    transcript.write_point(s_poly_commitment)?;

    // Challenge that will ensure that the prover cannot change P but can only
    // witness a random polynomial commitment that agrees with P at x_3, with high
    // probability.
    let xi = *transcript.squeeze_challenge_scalar::<()>();

    // Challenge that ensures that the prover did not interfere with the U term
    // in their commitments.
    let z = *transcript.squeeze_challenge_scalar::<()>();

    // We'll be opening `P' = P - [v] G_0 + [ξ] S` to ensure it has a root at
    // zero.
    let mut p_prime_poly = s_poly * xi + p_poly;
    let v = eval_polynomial(&p_prime_poly, x_3);
    p_prime_poly[0] = p_prime_poly[0] - &v;
    let p_prime_blind = s_poly_blind * Blind(xi) + p_blind;

    // This accumulates the synthetic blinding factor `f` starting
    // with the blinding factor for `P'`.
    let mut f = p_prime_blind.0;

    // Initialize the vector `p_prime` as the coefficients of the polynomial.
    let mut p_prime = p_prime_poly.values;
    assert_eq!(p_prime.len(), params.n as usize);

    // Initialize the vector `b` as the powers of `x_3`. The inner product of
    // `p_prime` and `b` is the evaluation of the polynomial at `x_3`.
    let mut b = Vec::with_capacity(1 << params.k);
    {
        let mut cur = C::Scalar::one();
        for _ in 0..(1 << params.k) {
            b.push(cur);
            cur *= &x_3;
        }
    }

    // Initialize the vector `G'` from the URS. We'll be progressively collapsing
    // this vector into smaller and smaller vectors until it is of length 1.
    let mut g_prime = params.g.clone();

    // Perform the inner product argument, round by round.
    for j in 0..params.k {
        let half = 1 << (params.k - j - 1); // half the length of `p_prime`, `b`, `G'`

        // Compute L, R
        //
        // TODO: If we modify multiexp to take "extra" bases, we could speed
        // this piece up a bit by combining the multiexps.
        let l_j = best_multiexp(&p_prime[half..], &g_prime[0..half]);
        let r_j = best_multiexp(&p_prime[0..half], &g_prime[half..]);
        let value_l_j = compute_inner_product(&p_prime[half..], &b[0..half]);
        let value_r_j = compute_inner_product(&p_prime[0..half], &b[half..]);
        let l_j_randomness = C::Scalar::random(&mut rng);
        let r_j_randomness = C::Scalar::random(&mut rng);
        let l_j = l_j + &best_multiexp(&[value_l_j * &z, l_j_randomness], &[params.u, params.w]);
        let r_j = r_j + &best_multiexp(&[value_r_j * &z, r_j_randomness], &[params.u, params.w]);
        let l_j = l_j.to_affine();
        let r_j = r_j.to_affine();

        // Feed L and R into the real transcript
        transcript.write_point(l_j)?;
        transcript.write_point(r_j)?;

        let u_j = *transcript.squeeze_challenge_scalar::<()>();
        let u_j_inv = u_j.invert().unwrap(); // TODO, bubble this up

        // Collapse `p_prime` and `b`.
        // TODO: parallelize
        for i in 0..half {
            p_prime[i] = p_prime[i] + &(p_prime[i + half] * &u_j_inv);
            b[i] = b[i] + &(b[i + half] * &u_j);
        }
        p_prime.truncate(half);
        b.truncate(half);

        // Collapse `G'`
        parallel_generator_collapse(&mut g_prime, u_j);
        g_prime.truncate(half);

        // Update randomness (the synthetic blinding factor at the end)
        f += &(l_j_randomness * &u_j_inv);
        f += &(r_j_randomness * &u_j);
    }

    // We have fully collapsed `p_prime`, `b`, `G'`
    assert_eq!(p_prime.len(), 1);
    let c = p_prime[0];

    transcript.write_scalar(c)?;
    transcript.write_scalar(f)?;

    Ok(())
}

fn parallel_generator_collapse<C: CurveAffine>(g: &mut [C], challenge: C::Scalar) {
    let len = g.len() / 2;
    let (g_lo, g_hi) = g.split_at_mut(len);

    parallelize(g_lo, |g_lo, start| {
        let g_hi = &g_hi[start..];
        let mut tmp = Vec::with_capacity(g_lo.len());
        for (g_lo, g_hi) in g_lo.iter().zip(g_hi.iter()) {
            tmp.push(g_lo.to_curve() + &(*g_hi * challenge));
        }
        C::Curve::batch_normalize(&tmp, g_lo);
    });
}