/* * Falcon signature generation. * * ==========================(LICENSE BEGIN)============================ * * Copyright (c) 2017-2019 Falcon Project * * Permission is hereby granted, free of charge, to any person obtaining * a copy of this software and associated documentation files (the * "Software"), to deal in the Software without restriction, including * without limitation the rights to use, copy, modify, merge, publish, * distribute, sublicense, and/or sell copies of the Software, and to * permit persons to whom the Software is furnished to do so, subject to * the following conditions: * * The above copyright notice and this permission notice shall be * included in all copies or substantial portions of the Software. * * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, * EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF * MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. * IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY * CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, * TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE * SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. * * ===========================(LICENSE END)============================= * * @author Thomas Pornin */ #include "inner.h" /* =================================================================== */ /* * Compute degree N from logarithm 'logn'. */ #define MKN(logn) ((size_t)1 << (logn)) /* =================================================================== */ /* * Binary case: * N = 2^logn * phi = X^N+1 */ /* * Get the size of the LDL tree for an input with polynomials of size * 2^logn. The size is expressed in the number of elements. */ static inline unsigned ffLDL_treesize(unsigned logn) { /* * For logn = 0 (polynomials are constant), the "tree" is a * single element. Otherwise, the tree node has size 2^logn, and * has two child trees for size logn-1 each. Thus, treesize s() * must fulfill these two relations: * * s(0) = 1 * s(logn) = (2^logn) + 2*s(logn-1) */ return (logn + 1) << logn; } /* * Inner function for ffLDL_fft(). It expects the matrix to be both * auto-adjoint and quasicyclic; also, it uses the source operands * as modifiable temporaries. * * tmp[] must have room for at least one polynomial. */ static void ffLDL_fft_inner(fpr *restrict tree, fpr *restrict g0, fpr *restrict g1, unsigned logn, fpr *restrict tmp) { size_t n, hn; n = MKN(logn); if (n == 1) { tree[0] = g0[0]; return; } hn = n >> 1; /* * The LDL decomposition yields L (which is written in the tree) * and the diagonal of D. Since d00 = g0, we just write d11 * into tmp. */ PQCLEAN_FALCON1024_CLEAN_poly_LDLmv_fft(tmp, tree, g0, g1, g0, logn); /* * Split d00 (currently in g0) and d11 (currently in tmp). We * reuse g0 and g1 as temporary storage spaces: * d00 splits into g1, g1+hn * d11 splits into g0, g0+hn */ PQCLEAN_FALCON1024_CLEAN_poly_split_fft(g1, g1 + hn, g0, logn); PQCLEAN_FALCON1024_CLEAN_poly_split_fft(g0, g0 + hn, tmp, logn); /* * Each split result is the first row of a new auto-adjoint * quasicyclic matrix for the next recursive step. */ ffLDL_fft_inner(tree + n, g1, g1 + hn, logn - 1, tmp); ffLDL_fft_inner(tree + n + ffLDL_treesize(logn - 1), g0, g0 + hn, logn - 1, tmp); } /* * Compute the ffLDL tree of an auto-adjoint matrix G. The matrix * is provided as three polynomials (FFT representation). * * The "tree" array is filled with the computed tree, of size * (logn+1)*(2^logn) elements (see ffLDL_treesize()). * * Input arrays MUST NOT overlap, except possibly the three unmodified * arrays g00, g01 and g11. tmp[] should have room for at least three * polynomials of 2^logn elements each. */ static void ffLDL_fft(fpr *restrict tree, const fpr *restrict g00, const fpr *restrict g01, const fpr *restrict g11, unsigned logn, fpr *restrict tmp) { size_t n, hn; fpr *d00, *d11; n = MKN(logn); if (n == 1) { tree[0] = g00[0]; return; } hn = n >> 1; d00 = tmp; d11 = tmp + n; tmp += n << 1; memcpy(d00, g00, n * sizeof * g00); PQCLEAN_FALCON1024_CLEAN_poly_LDLmv_fft(d11, tree, g00, g01, g11, logn); PQCLEAN_FALCON1024_CLEAN_poly_split_fft(tmp, tmp + hn, d00, logn); PQCLEAN_FALCON1024_CLEAN_poly_split_fft(d00, d00 + hn, d11, logn); memcpy(d11, tmp, n * sizeof * tmp); ffLDL_fft_inner(tree + n, d11, d11 + hn, logn - 1, tmp); ffLDL_fft_inner(tree + n + ffLDL_treesize(logn - 1), d00, d00 + hn, logn - 1, tmp); } /* * Normalize an ffLDL tree: each leaf of value x is replaced with * sigma / sqrt(x). */ static void ffLDL_binary_normalize(fpr *tree, unsigned logn) { /* * TODO: make an iterative version. */ size_t n; n = MKN(logn); if (n == 1) { /* * We actually store in the tree leaf the inverse of * the value mandated by the specification: this * saves a division both here and in the sampler. */ tree[0] = fpr_mul(fpr_sqrt(tree[0]), fpr_inv_sigma); } else { ffLDL_binary_normalize(tree + n, logn - 1); ffLDL_binary_normalize(tree + n + ffLDL_treesize(logn - 1), logn - 1); } } /* =================================================================== */ /* * Convert an integer polynomial (with small values) into the * representation with complex numbers. */ static void smallints_to_fpr(fpr *r, const int8_t *t, unsigned logn) { size_t n, u; n = MKN(logn); for (u = 0; u < n; u ++) { r[u] = fpr_of(t[u]); } } /* * The expanded private key contains: * - The B0 matrix (four elements) * - The ffLDL tree */ static inline size_t skoff_b00(unsigned logn) { (void)logn; return 0; } static inline size_t skoff_b01(unsigned logn) { return MKN(logn); } static inline size_t skoff_b10(unsigned logn) { return 2 * MKN(logn); } static inline size_t skoff_b11(unsigned logn) { return 3 * MKN(logn); } static inline size_t skoff_tree(unsigned logn) { return 4 * MKN(logn); } /* see inner.h */ void PQCLEAN_FALCON1024_CLEAN_expand_privkey(fpr *restrict sk, const int8_t *f_src, const int8_t *g_src, const int8_t *F_src, const int8_t *G_src, unsigned logn, uint8_t *restrict tmp) { size_t n; fpr *f, *g, *F, *G; fpr *b00, *b01, *b10, *b11; fpr *g00, *g01, *g11, *gxx; fpr *tree; n = MKN(logn); b00 = sk + skoff_b00(logn); b01 = sk + skoff_b01(logn); b10 = sk + skoff_b10(logn); b11 = sk + skoff_b11(logn); tree = sk + skoff_tree(logn); /* * We load the private key elements directly into the B0 matrix, * since B0 = [[g, -f], [G, -F]]. */ f = b01; g = b00; F = b11; G = b10; smallints_to_fpr(f, f_src, logn); smallints_to_fpr(g, g_src, logn); smallints_to_fpr(F, F_src, logn); smallints_to_fpr(G, G_src, logn); /* * Compute the FFT for the key elements, and negate f and F. */ PQCLEAN_FALCON1024_CLEAN_FFT(f, logn); PQCLEAN_FALCON1024_CLEAN_FFT(g, logn); PQCLEAN_FALCON1024_CLEAN_FFT(F, logn); PQCLEAN_FALCON1024_CLEAN_FFT(G, logn); PQCLEAN_FALCON1024_CLEAN_poly_neg(f, logn); PQCLEAN_FALCON1024_CLEAN_poly_neg(F, logn); /* * The Gram matrix is G = B·B*. Formulas are: * g00 = b00*adj(b00) + b01*adj(b01) * g01 = b00*adj(b10) + b01*adj(b11) * g10 = b10*adj(b00) + b11*adj(b01) * g11 = b10*adj(b10) + b11*adj(b11) * * For historical reasons, this implementation uses * g00, g01 and g11 (upper triangle). */ g00 = (fpr *)tmp; g01 = g00 + n; g11 = g01 + n; gxx = g11 + n; memcpy(g00, b00, n * sizeof * b00); PQCLEAN_FALCON1024_CLEAN_poly_mulselfadj_fft(g00, logn); memcpy(gxx, b01, n * sizeof * b01); PQCLEAN_FALCON1024_CLEAN_poly_mulselfadj_fft(gxx, logn); PQCLEAN_FALCON1024_CLEAN_poly_add(g00, gxx, logn); memcpy(g01, b00, n * sizeof * b00); PQCLEAN_FALCON1024_CLEAN_poly_muladj_fft(g01, b10, logn); memcpy(gxx, b01, n * sizeof * b01); PQCLEAN_FALCON1024_CLEAN_poly_muladj_fft(gxx, b11, logn); PQCLEAN_FALCON1024_CLEAN_poly_add(g01, gxx, logn); memcpy(g11, b10, n * sizeof * b10); PQCLEAN_FALCON1024_CLEAN_poly_mulselfadj_fft(g11, logn); memcpy(gxx, b11, n * sizeof * b11); PQCLEAN_FALCON1024_CLEAN_poly_mulselfadj_fft(gxx, logn); PQCLEAN_FALCON1024_CLEAN_poly_add(g11, gxx, logn); /* * Compute the Falcon tree. */ ffLDL_fft(tree, g00, g01, g11, logn, gxx); /* * Normalize tree. */ ffLDL_binary_normalize(tree, logn); } typedef int (*samplerZ)(void *ctx, fpr mu, fpr sigma); /* * Perform Fast Fourier Sampling for target vector t. The Gram matrix * is provided (G = [[g00, g01], [adj(g01), g11]]). The sampled vector * is written over (t0,t1). The Gram matrix is modified as well. The * tmp[] buffer must have room for four polynomials. */ static void ffSampling_fft_dyntree(samplerZ samp, void *samp_ctx, fpr *restrict t0, fpr *restrict t1, fpr *restrict g00, fpr *restrict g01, fpr *restrict g11, unsigned logn, fpr *restrict tmp) { size_t n, hn; fpr *z0, *z1; /* * Deepest level: the LDL tree leaf value is just g00 (the * array has length only 1 at this point); we normalize it * with regards to sigma, then use it for sampling. */ if (logn == 0) { fpr leaf; leaf = g00[0]; leaf = fpr_mul(fpr_sqrt(leaf), fpr_inv_sigma); t0[0] = fpr_of(samp(samp_ctx, t0[0], leaf)); t1[0] = fpr_of(samp(samp_ctx, t1[0], leaf)); return; } n = (size_t)1 << logn; hn = n >> 1; /* * Decompose G into LDL. We only need d00 (identical to g00), * d11, and l10; we do that in place. */ PQCLEAN_FALCON1024_CLEAN_poly_LDL_fft(g00, g01, g11, logn); /* * Split d00 and d11 and expand them into half-size quasi-cyclic * Gram matrices. We also save l10 in tmp[]. */ PQCLEAN_FALCON1024_CLEAN_poly_split_fft(tmp, tmp + hn, g00, logn); memcpy(g00, tmp, n * sizeof * tmp); PQCLEAN_FALCON1024_CLEAN_poly_split_fft(tmp, tmp + hn, g11, logn); memcpy(g11, tmp, n * sizeof * tmp); memcpy(tmp, g01, n * sizeof * g01); memcpy(g01, g00, hn * sizeof * g00); memcpy(g01 + hn, g11, hn * sizeof * g00); /* * The half-size Gram matrices for the recursive LDL tree * building are now: * - left sub-tree: g00, g00+hn, g01 * - right sub-tree: g11, g11+hn, g01+hn * l10 is in tmp[]. */ /* * We split t1 and use the first recursive call on the two * halves, using the right sub-tree. The result is merged * back into tmp + 2*n. */ z1 = tmp + n; PQCLEAN_FALCON1024_CLEAN_poly_split_fft(z1, z1 + hn, t1, logn); ffSampling_fft_dyntree(samp, samp_ctx, z1, z1 + hn, g11, g11 + hn, g01 + hn, logn - 1, z1 + n); PQCLEAN_FALCON1024_CLEAN_poly_merge_fft(tmp + (n << 1), z1, z1 + hn, logn); /* * Compute tb0 = t0 + (t1 - z1) * l10. * At that point, l10 is in tmp, t1 is unmodified, and z1 is * in tmp + (n << 1). The buffer in z1 is free. * * In the end, z1 is written over t1, and tb0 is in t0. */ memcpy(z1, t1, n * sizeof * t1); PQCLEAN_FALCON1024_CLEAN_poly_sub(z1, tmp + (n << 1), logn); memcpy(t1, tmp + (n << 1), n * sizeof * tmp); PQCLEAN_FALCON1024_CLEAN_poly_mul_fft(tmp, z1, logn); PQCLEAN_FALCON1024_CLEAN_poly_add(t0, tmp, logn); /* * Second recursive invocation, on the split tb0 (currently in t0) * and the left sub-tree. */ z0 = tmp; PQCLEAN_FALCON1024_CLEAN_poly_split_fft(z0, z0 + hn, t0, logn); ffSampling_fft_dyntree(samp, samp_ctx, z0, z0 + hn, g00, g00 + hn, g01, logn - 1, z0 + n); PQCLEAN_FALCON1024_CLEAN_poly_merge_fft(t0, z0, z0 + hn, logn); } /* * Perform Fast Fourier Sampling for target vector t and LDL tree T. * tmp[] must have size for at least two polynomials of size 2^logn. */ static void ffSampling_fft(samplerZ samp, void *samp_ctx, fpr *restrict z0, fpr *restrict z1, const fpr *restrict tree, const fpr *restrict t0, const fpr *restrict t1, unsigned logn, fpr *restrict tmp) { size_t n, hn; const fpr *tree0, *tree1; n = (size_t)1 << logn; if (n == 1) { fpr x0, x1, sigma; x0 = t0[0]; x1 = t1[0]; sigma = tree[0]; z0[0] = fpr_of(samp(samp_ctx, x0, sigma)); z1[0] = fpr_of(samp(samp_ctx, x1, sigma)); return; } hn = n >> 1; tree0 = tree + n; tree1 = tree + n + ffLDL_treesize(logn - 1); /* * We split t1 into z1 (reused as temporary storage), then do * the recursive invocation, with output in tmp. We finally * merge back into z1. */ PQCLEAN_FALCON1024_CLEAN_poly_split_fft(z1, z1 + hn, t1, logn); ffSampling_fft(samp, samp_ctx, tmp, tmp + hn, tree1, z1, z1 + hn, logn - 1, tmp + n); PQCLEAN_FALCON1024_CLEAN_poly_merge_fft(z1, tmp, tmp + hn, logn); /* * Compute tb0 = t0 + (t1 - z1) * L. Value tb0 ends up in tmp[]. */ memcpy(tmp, t1, n * sizeof * t1); PQCLEAN_FALCON1024_CLEAN_poly_sub(tmp, z1, logn); PQCLEAN_FALCON1024_CLEAN_poly_mul_fft(tmp, tree, logn); PQCLEAN_FALCON1024_CLEAN_poly_add(tmp, t0, logn); /* * Second recursive invocation. */ PQCLEAN_FALCON1024_CLEAN_poly_split_fft(z0, z0 + hn, tmp, logn); ffSampling_fft(samp, samp_ctx, tmp, tmp + hn, tree0, z0, z0 + hn, logn - 1, tmp + n); PQCLEAN_FALCON1024_CLEAN_poly_merge_fft(z0, tmp, tmp + hn, logn); } /* * Compute a signature: the signature contains two vectors, s1 and s2; * the caller must still check that they comply with the signature * bound, and try again if that is not the case. The s1 vector is not * returned; instead, its squared norm (saturated) is returned. This * function uses an expanded key. * * tmp[] must have room for at least six polynomials. */ static uint32_t do_sign_tree(samplerZ samp, void *samp_ctx, int16_t *s2, const fpr *restrict expanded_key, const uint16_t *hm, unsigned logn, fpr *restrict tmp) { size_t n, u; fpr *t0, *t1, *tx, *ty; const fpr *b00, *b01, *b10, *b11, *tree; fpr ni; uint32_t sqn, ng; n = MKN(logn); t0 = tmp; t1 = t0 + n; b00 = expanded_key + skoff_b00(logn); b01 = expanded_key + skoff_b01(logn); b10 = expanded_key + skoff_b10(logn); b11 = expanded_key + skoff_b11(logn); tree = expanded_key + skoff_tree(logn); /* * Set the target vector to [hm, 0] (hm is the hashed message). */ for (u = 0; u < n; u ++) { t0[u] = fpr_of(hm[u]); /* This is implicit. t1[u] = fpr_zero; */ } /* * Apply the lattice basis to obtain the real target * vector (after normalization with regards to modulus). */ PQCLEAN_FALCON1024_CLEAN_FFT(t0, logn); ni = fpr_inverse_of_q; memcpy(t1, t0, n * sizeof * t0); PQCLEAN_FALCON1024_CLEAN_poly_mul_fft(t1, b01, logn); PQCLEAN_FALCON1024_CLEAN_poly_mulconst(t1, fpr_neg(ni), logn); PQCLEAN_FALCON1024_CLEAN_poly_mul_fft(t0, b11, logn); PQCLEAN_FALCON1024_CLEAN_poly_mulconst(t0, ni, logn); tx = t1 + n; ty = tx + n; /* * Apply sampling. Output is written back in [tx, ty]. */ ffSampling_fft(samp, samp_ctx, tx, ty, tree, t0, t1, logn, ty + n); /* * Get the lattice point corresponding to that tiny vector. */ memcpy(t0, tx, n * sizeof * tx); memcpy(t1, ty, n * sizeof * ty); PQCLEAN_FALCON1024_CLEAN_poly_mul_fft(tx, b00, logn); PQCLEAN_FALCON1024_CLEAN_poly_mul_fft(ty, b10, logn); PQCLEAN_FALCON1024_CLEAN_poly_add(tx, ty, logn); memcpy(ty, t0, n * sizeof * t0); PQCLEAN_FALCON1024_CLEAN_poly_mul_fft(ty, b01, logn); memcpy(t0, tx, n * sizeof * tx); PQCLEAN_FALCON1024_CLEAN_poly_mul_fft(t1, b11, logn); PQCLEAN_FALCON1024_CLEAN_poly_add(t1, ty, logn); PQCLEAN_FALCON1024_CLEAN_iFFT(t0, logn); PQCLEAN_FALCON1024_CLEAN_iFFT(t1, logn); /* * Compute the signature. */ sqn = 0; ng = 0; for (u = 0; u < n; u ++) { int32_t z; z = (int32_t)hm[u] - (int32_t)fpr_rint(t0[u]); sqn += (uint32_t)(z * z); ng |= sqn; } sqn |= -(ng >> 31); for (u = 0; u < n; u ++) { s2[u] = (int16_t) - fpr_rint(t1[u]); } return sqn; } /* * Compute a signature: the signature contains two vectors, s1 and s2; * the caller must still check that they comply with the signature * bound, and try again if that is not the case. The s1 vector is not * returned; instead, its squared norm (saturated) is returned. This * function uses a raw key and recomputes the B0 matrix and LDL tree * dynamically. * * tmp[] must have room for at least nine polynomials. */ static uint32_t do_sign_dyn(samplerZ samp, void *samp_ctx, int16_t *s2, const int8_t *restrict f, const int8_t *restrict g, const int8_t *restrict F, const int8_t *restrict G, const uint16_t *hm, unsigned logn, fpr *restrict tmp) { size_t n, u; fpr *t0, *t1, *tx, *ty; fpr *b00, *b01, *b10, *b11, *g00, *g01, *g11; fpr ni; uint32_t sqn, ng; n = MKN(logn); /* * Lattice basis is B = [[g, -f], [G, -F]]. We convert it to FFT. */ b00 = tmp; b01 = b00 + n; b10 = b01 + n; b11 = b10 + n; smallints_to_fpr(b01, f, logn); smallints_to_fpr(b00, g, logn); smallints_to_fpr(b11, F, logn); smallints_to_fpr(b10, G, logn); PQCLEAN_FALCON1024_CLEAN_FFT(b01, logn); PQCLEAN_FALCON1024_CLEAN_FFT(b00, logn); PQCLEAN_FALCON1024_CLEAN_FFT(b11, logn); PQCLEAN_FALCON1024_CLEAN_FFT(b10, logn); PQCLEAN_FALCON1024_CLEAN_poly_neg(b01, logn); PQCLEAN_FALCON1024_CLEAN_poly_neg(b11, logn); /* * Compute the Gram matrix G = B·B*. Formulas are: * g00 = b00*adj(b00) + b01*adj(b01) * g01 = b00*adj(b10) + b01*adj(b11) * g10 = b10*adj(b00) + b11*adj(b01) * g11 = b10*adj(b10) + b11*adj(b11) * * For historical reasons, this implementation uses * g00, g01 and g11 (upper triangle). g10 is not kept * since it is equal to adj(g01). * * We _replace_ the matrix B with the Gram matrix, but we * must keep b01 and b11 for computing the target vector. */ t0 = b11 + n; t1 = t0 + n; memcpy(t0, b01, n * sizeof * b01); PQCLEAN_FALCON1024_CLEAN_poly_mulselfadj_fft(t0, logn); // t0 <- b01*adj(b01) memcpy(t1, b00, n * sizeof * b00); PQCLEAN_FALCON1024_CLEAN_poly_muladj_fft(t1, b10, logn); // t1 <- b00*adj(b10) PQCLEAN_FALCON1024_CLEAN_poly_mulselfadj_fft(b00, logn); // b00 <- b00*adj(b00) PQCLEAN_FALCON1024_CLEAN_poly_add(b00, t0, logn); // b00 <- g00 memcpy(t0, b01, n * sizeof * b01); PQCLEAN_FALCON1024_CLEAN_poly_muladj_fft(b01, b11, logn); // b01 <- b01*adj(b11) PQCLEAN_FALCON1024_CLEAN_poly_add(b01, t1, logn); // b01 <- g01 PQCLEAN_FALCON1024_CLEAN_poly_mulselfadj_fft(b10, logn); // b10 <- b10*adj(b10) memcpy(t1, b11, n * sizeof * b11); PQCLEAN_FALCON1024_CLEAN_poly_mulselfadj_fft(t1, logn); // t1 <- b11*adj(b11) PQCLEAN_FALCON1024_CLEAN_poly_add(b10, t1, logn); // b10 <- g11 /* * We rename variables to make things clearer. The three elements * of the Gram matrix uses the first 3*n slots of tmp[], followed * by b11 and b01 (in that order). */ g00 = b00; g01 = b01; g11 = b10; b01 = t0; t0 = b01 + n; t1 = t0 + n; /* * Memory layout at that point: * g00 g01 g11 b11 b01 t0 t1 */ /* * Set the target vector to [hm, 0] (hm is the hashed message). */ for (u = 0; u < n; u ++) { t0[u] = fpr_of(hm[u]); /* This is implicit. t1[u] = fpr_zero; */ } /* * Apply the lattice basis to obtain the real target * vector (after normalization with regards to modulus). */ PQCLEAN_FALCON1024_CLEAN_FFT(t0, logn); ni = fpr_inverse_of_q; memcpy(t1, t0, n * sizeof * t0); PQCLEAN_FALCON1024_CLEAN_poly_mul_fft(t1, b01, logn); PQCLEAN_FALCON1024_CLEAN_poly_mulconst(t1, fpr_neg(ni), logn); PQCLEAN_FALCON1024_CLEAN_poly_mul_fft(t0, b11, logn); PQCLEAN_FALCON1024_CLEAN_poly_mulconst(t0, ni, logn); /* * b01 and b11 can be discarded, so we move back (t0,t1). * Memory layout is now: * g00 g01 g11 t0 t1 */ memcpy(b11, t0, n * 2 * sizeof * t0); t0 = g11 + n; t1 = t0 + n; /* * Apply sampling; result is written over (t0,t1). */ ffSampling_fft_dyntree(samp, samp_ctx, t0, t1, g00, g01, g11, logn, t1 + n); /* * We arrange the layout back to: * b00 b01 b10 b11 t0 t1 * * We did not conserve the matrix basis, so we must recompute * it now. */ b00 = tmp; b01 = b00 + n; b10 = b01 + n; b11 = b10 + n; memmove(b11 + n, t0, n * 2 * sizeof * t0); t0 = b11 + n; t1 = t0 + n; smallints_to_fpr(b01, f, logn); smallints_to_fpr(b00, g, logn); smallints_to_fpr(b11, F, logn); smallints_to_fpr(b10, G, logn); PQCLEAN_FALCON1024_CLEAN_FFT(b01, logn); PQCLEAN_FALCON1024_CLEAN_FFT(b00, logn); PQCLEAN_FALCON1024_CLEAN_FFT(b11, logn); PQCLEAN_FALCON1024_CLEAN_FFT(b10, logn); PQCLEAN_FALCON1024_CLEAN_poly_neg(b01, logn); PQCLEAN_FALCON1024_CLEAN_poly_neg(b11, logn); tx = t1 + n; ty = tx + n; /* * Get the lattice point corresponding to that tiny vector. */ memcpy(tx, t0, n * sizeof * t0); memcpy(ty, t1, n * sizeof * t1); PQCLEAN_FALCON1024_CLEAN_poly_mul_fft(tx, b00, logn); PQCLEAN_FALCON1024_CLEAN_poly_mul_fft(ty, b10, logn); PQCLEAN_FALCON1024_CLEAN_poly_add(tx, ty, logn); memcpy(ty, t0, n * sizeof * t0); PQCLEAN_FALCON1024_CLEAN_poly_mul_fft(ty, b01, logn); memcpy(t0, tx, n * sizeof * tx); PQCLEAN_FALCON1024_CLEAN_poly_mul_fft(t1, b11, logn); PQCLEAN_FALCON1024_CLEAN_poly_add(t1, ty, logn); PQCLEAN_FALCON1024_CLEAN_iFFT(t0, logn); PQCLEAN_FALCON1024_CLEAN_iFFT(t1, logn); sqn = 0; ng = 0; for (u = 0; u < n; u ++) { int32_t z; z = (int32_t)hm[u] - (int32_t)fpr_rint(t0[u]); sqn += (uint32_t)(z * z); ng |= sqn; } sqn |= -(ng >> 31); for (u = 0; u < n; u ++) { s2[u] = (int16_t) - fpr_rint(t1[u]); } return sqn; } /* * Sample an integer value along a half-gaussian distribution centered * on zero and standard deviation 1.8205, with a precision of 72 bits. */ static int gaussian0_sampler(prng *p) { static const uint32_t dist[] = { 6031371U, 13708371U, 13035518U, 5186761U, 1487980U, 12270720U, 3298653U, 4688887U, 5511555U, 1551448U, 9247616U, 9467675U, 539632U, 14076116U, 5909365U, 138809U, 10836485U, 13263376U, 26405U, 15335617U, 16601723U, 3714U, 14514117U, 13240074U, 386U, 8324059U, 3276722U, 29U, 12376792U, 7821247U, 1U, 11611789U, 3398254U, 0U, 1194629U, 4532444U, 0U, 37177U, 2973575U, 0U, 855U, 10369757U, 0U, 14U, 9441597U, 0U, 0U, 3075302U, 0U, 0U, 28626U, 0U, 0U, 197U, 0U, 0U, 1U }; uint32_t v0, v1, v2, hi; uint64_t lo; size_t u; int z; /* * Get a random 72-bit value, into three 24-bit limbs v0..v2. */ lo = prng_get_u64(p); hi = prng_get_u8(p); v0 = (uint32_t)lo & 0xFFFFFF; v1 = (uint32_t)(lo >> 24) & 0xFFFFFF; v2 = (uint32_t)(lo >> 48) | (hi << 16); /* * Sampled value is z, such that v0..v2 is lower than the first * z elements of the table. */ z = 0; for (u = 0; u < (sizeof dist) / sizeof(dist[0]); u += 3) { uint32_t w0, w1, w2, cc; w0 = dist[u + 2]; w1 = dist[u + 1]; w2 = dist[u + 0]; cc = (v0 - w0) >> 31; cc = (v1 - w1 - cc) >> 31; cc = (v2 - w2 - cc) >> 31; z += (int)cc; } return z; } /* * Sample a bit with probability exp(-x) for some x >= 0. */ static int BerExp(prng *p, fpr x) { int s, i; fpr r; uint32_t sw, w; uint64_t z; /* * Reduce x modulo log(2): x = s*log(2) + r, with s an integer, * and 0 <= r < log(2). Since x >= 0, we can use fpr_trunc(). */ s = (int)fpr_trunc(fpr_mul(x, fpr_inv_log2)); r = fpr_sub(x, fpr_mul(fpr_of(s), fpr_log2)); /* * It may happen (quite rarely) that s >= 64; if sigma = 1.2 * (the minimum value for sigma), r = 0 and b = 1, then we get * s >= 64 if the half-Gaussian produced a z >= 13, which happens * with probability about 0.000000000230383991, which is * approximatively equal to 2^(-32). In any case, if s >= 64, * then BerExp will be non-zero with probability less than * 2^(-64), so we can simply saturate s at 63. */ sw = (uint32_t)s; sw ^= (sw ^ 63) & -((63 - sw) >> 31); s = (int)sw; /* * Compute exp(-r); we know that 0 <= r < log(2) at this point, so * we can use fpr_expm_p63(), which yields a result scaled to 2^63. * We scale it up to 2^64, then right-shift it by s bits because * we really want exp(-x) = 2^(-s)*exp(-r). * * The "-1" operation makes sure that the value fits on 64 bits * (i.e. if r = 0, we may get 2^64, and we prefer 2^64-1 in that * case). The bias is negligible since fpr_expm_p63() only computes * with 51 bits of precision or so. */ z = ((fpr_expm_p63(r) << 1) - 1) >> s; /* * Sample a bit with probability exp(-x). Since x = s*log(2) + r, * exp(-x) = 2^-s * exp(-r), we compare lazily exp(-x) with the * PRNG output to limit its consumption, the sign of the difference * yields the expected result. */ i = 64; do { i -= 8; w = prng_get_u8(p) - ((uint32_t)(z >> i) & 0xFF); } while (!w && i > 0); return (int)(w >> 31); } typedef struct { prng p; fpr sigma_min; } sampler_context; /* * The sampler produces a random integer that follows a discrete Gaussian * distribution, centered on mu, and with standard deviation sigma. The * provided parameter isigma is equal to 1/sigma. * * The value of sigma MUST lie between 1 and 2 (i.e. isigma lies between * 0.5 and 1); in Falcon, sigma should always be between 1.2 and 1.9. */ static int sampler(void *ctx, fpr mu, fpr isigma) { sampler_context *spc; int s; fpr r, dss, ccs; spc = ctx; /* * Center is mu. We compute mu = s + r where s is an integer * and 0 <= r < 1. */ s = (int)fpr_floor(mu); r = fpr_sub(mu, fpr_of(s)); /* * dss = 1/(2*sigma^2) = 0.5*(isigma^2). */ dss = fpr_half(fpr_sqr(isigma)); /* * ccs = sigma_min / sigma = sigma_min * isigma. */ ccs = fpr_mul(isigma, spc->sigma_min); /* * We now need to sample on center r. */ for (;;) { int z0, z, b; fpr x; /* * Sample z for a Gaussian distribution. Then get a * random bit b to turn the sampling into a bimodal * distribution: if b = 1, we use z+1, otherwise we * use -z. We thus have two situations: * * - b = 1: z >= 1 and sampled against a Gaussian * centered on 1. * - b = 0: z <= 0 and sampled against a Gaussian * centered on 0. */ z0 = gaussian0_sampler(&spc->p); b = prng_get_u8(&spc->p) & 1; z = b + ((b << 1) - 1) * z0; /* * Rejection sampling. We want a Gaussian centered on r; * but we sampled against a Gaussian centered on b (0 or * 1). But we know that z is always in the range where * our sampling distribution is greater than the Gaussian * distribution, so rejection works. * * We got z with distribution: * G(z) = exp(-((z-b)^2)/(2*sigma0^2)) * We target distribution: * S(z) = exp(-((z-r)^2)/(2*sigma^2)) * Rejection sampling works by keeping the value z with * probability S(z)/G(z), and starting again otherwise. * This requires S(z) <= G(z), which is the case here. * Thus, we simply need to keep our z with probability: * P = exp(-x) * where: * x = ((z-r)^2)/(2*sigma^2) - ((z-b)^2)/(2*sigma0^2) * * Here, we scale up the Bernouilli distribution, which * makes rejection more probable, but makes rejection * rate sufficiently decorrelated from the Gaussian * center and standard deviation that the whole sampler * can be said to be constant-time. */ x = fpr_mul(fpr_sqr(fpr_sub(fpr_of(z), r)), dss); x = fpr_sub(x, fpr_mul(fpr_of(z0 * z0), fpr_inv_2sqrsigma0)); x = fpr_mul(x, ccs); if (BerExp(&spc->p, x)) { /* * Rejection sampling was centered on r, but the * actual center is mu = s + r. */ return s + z; } } } /* see inner.h */ void PQCLEAN_FALCON1024_CLEAN_sign_tree(int16_t *sig, shake256_context *rng, const fpr *restrict expanded_key, const uint16_t *hm, unsigned logn, uint8_t *tmp) { fpr *ftmp; ftmp = (fpr *)tmp; for (;;) { /* * Signature produces short vectors s1 and s2. The * signature is acceptable only if the aggregate vector * s1,s2 is short; we must use the same bound as the * verifier. * * If the signature is acceptable, then we return only s2 * (the verifier recomputes s1 from s2, the hashed message, * and the public key). */ sampler_context spc; samplerZ samp; void *samp_ctx; uint32_t sqn; /* * Normal sampling. We use a fast PRNG seeded from our * SHAKE context ('rng'). */ spc.sigma_min = (logn == 10) ? fpr_sigma_min_10 : fpr_sigma_min_9; PQCLEAN_FALCON1024_CLEAN_prng_init(&spc.p, rng); samp = sampler; samp_ctx = &spc; /* * Do the actual signature. */ sqn = do_sign_tree(samp, samp_ctx, sig, expanded_key, hm, logn, ftmp); /* * Check that the norm is correct. With our chosen * acceptance bound, this should almost always be true. * With a tighter bound, it may happen sometimes that we * end up with an invalidly large signature, in which * case we just loop. */ if (PQCLEAN_FALCON1024_CLEAN_is_short_half(sqn, sig, logn)) { break; } } } /* see inner.h */ void PQCLEAN_FALCON1024_CLEAN_sign_dyn(int16_t *sig, shake256_context *rng, const int8_t *restrict f, const int8_t *restrict g, const int8_t *restrict F, const int8_t *restrict G, const uint16_t *hm, unsigned logn, uint8_t *tmp) { fpr *ftmp; ftmp = (fpr *)tmp; for (;;) { /* * Signature produces short vectors s1 and s2. The * signature is acceptable only if the aggregate vector * s1,s2 is short; we must use the same bound as the * verifier. * * If the signature is acceptable, then we return only s2 * (the verifier recomputes s1 from s2, the hashed message, * and the public key). */ sampler_context spc; samplerZ samp; void *samp_ctx; uint32_t sqn; /* * Normal sampling. We use a fast PRNG seeded from our * SHAKE context ('rng'). */ spc.sigma_min = (logn == 10) ? fpr_sigma_min_10 : fpr_sigma_min_9; PQCLEAN_FALCON1024_CLEAN_prng_init(&spc.p, rng); samp = sampler; samp_ctx = &spc; /* * Do the actual signature. */ sqn = do_sign_dyn(samp, samp_ctx, sig, f, g, F, G, hm, logn, ftmp); /* * Check that the norm is correct. With our chosen * acceptance bound, this should almost always be true. * With a tighter bound, it may happen sometimes that we * end up with an invalidly large signature, in which * case we just loop. */ if (PQCLEAN_FALCON1024_CLEAN_is_short_half(sqn, sig, logn)) { break; } } }