5079590438

Committed 25 May 2023 12:28PM UTC coverage: 92.228% (+0.5%) from 91.723%

Build # 5079590438

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Pull #3502

github

Pull Request Pull Request #3502: Apply clang-format to the codebase

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/src/lib/pubkey/mce/code_based_key_gen.cpp

/*
 * (C) Copyright Projet SECRET, INRIA, Rocquencourt
 * (C) Bhaskar Biswas and  Nicolas Sendrier
 *
 * (C) 2014 cryptosource GmbH
 * (C) 2014 Falko Strenzke fstrenzke@cryptosource.de
 * (C) 2015 Jack Lloyd
 *
 * Botan is released under the Simplified BSD License (see license.txt)
 *
 */

#include <botan/mceliece.h>

#include <botan/internal/code_based_util.h>
#include <botan/internal/loadstor.h>
#include <botan/internal/mce_internal.h>
#include <botan/internal/polyn_gf2m.h>

namespace Botan {

namespace {

class binary_matrix final {
   public:
      binary_matrix(size_t m_rown, size_t m_coln);

      void row_xor(size_t a, size_t b);
      secure_vector<size_t> row_reduced_echelon_form();

      /**
      * return the coefficient out of F_2
      */
      uint32_t coef(size_t i, size_t j) { return (m_elem[(i)*m_rwdcnt + (j) / 32] >> (j % 32)) & 1; }

      void set_coef_to_one(size_t i, size_t j) {
         m_elem[(i)*m_rwdcnt + (j) / 32] |= (static_cast<uint32_t>(1) << ((j) % 32));
      }

      void toggle_coeff(size_t i, size_t j) {
         m_elem[(i)*m_rwdcnt + (j) / 32] ^= (static_cast<uint32_t>(1) << ((j) % 32));
      }

      size_t rows() const { return m_rown; }

      size_t columns() const { return m_coln; }

      const std::vector<uint32_t>& elem() const { return m_elem; }

   private:
      size_t m_rown;    // number of rows.
      size_t m_coln;    // number of columns.
      size_t m_rwdcnt;  // number of words in a row
      std::vector<uint32_t> m_elem;
};

binary_matrix::binary_matrix(size_t rown, size_t coln) {
   m_coln = coln;
   m_rown = rown;
   m_rwdcnt = 1 + ((m_coln - 1) / 32);
   m_elem = std::vector<uint32_t>(m_rown * m_rwdcnt);
}

void binary_matrix::row_xor(size_t a, size_t b) {
   for(size_t i = 0; i != m_rwdcnt; i++) {
      m_elem[a * m_rwdcnt + i] ^= m_elem[b * m_rwdcnt + i];
   }
}

//the matrix is reduced from LSB...(from right)
secure_vector<size_t> binary_matrix::row_reduced_echelon_form() {
   secure_vector<size_t> perm(m_coln);
   for(size_t i = 0; i != m_coln; i++) {
      perm[i] = i;  // initialize permutation.
   }

   uint32_t failcnt = 0;

   size_t max = m_coln - 1;
   for(size_t i = 0; i != m_rown; i++, max--) {
      bool found_row = false;

      for(size_t j = i; !found_row && j != m_rown; j++) {
         if(coef(j, max)) {
            if(i != j)  //not needed as ith row is 0 and jth row is 1.
            {
               row_xor(i, j);  //xor to the row.(swap)?
            }

            found_row = true;
         }
      }

      //if no row with a 1 found then swap last column and the column with no 1 down.
      if(!found_row) {
         perm[m_coln - m_rown - 1 - failcnt] = static_cast<int>(max);
         failcnt++;
         if(!max) {
            perm.clear();
         }
         i--;
      } else {
         perm[i + m_coln - m_rown] = max;
         for(size_t j = i + 1; j < m_rown; j++)  //fill the column downwards with 0's
         {
            if(coef(j, max)) {
               row_xor(j, i);  //check the arg. order.
            }
         }

         //fill the column with 0's upwards too.
         for(size_t j = i; j != 0; --j) {
            if(coef(j - 1, max)) {
               row_xor(j - 1, i);
            }
         }
      }
   }  //end for(i)
   return perm;
}

void randomize_support(std::vector<gf2m>& L, RandomNumberGenerator& rng) {
   for(size_t i = 0; i != L.size(); ++i) {
      gf2m rnd = random_gf2m(rng);

      // no rejection sampling, but for useful code-based parameters with n <= 13 this seem tolerable
      std::swap(L[i], L[rnd % L.size()]);
   }
}

std::unique_ptr<binary_matrix> generate_R(
   std::vector<gf2m>& L, polyn_gf2m* g, const GF2m_Field& sp_field, size_t code_length, size_t t) {
   //L- Support
   //t- Number of errors
   //n- Length of the Goppa code
   //m- The extension degree of the GF
   //g- The generator polynomial.

   const size_t r = t * sp_field.get_extension_degree();

   binary_matrix H(r, code_length);

   for(size_t i = 0; i != code_length; i++) {
      gf2m x = g->eval(lex_to_gray(L[i]));  //evaluate the polynomial at the point L[i].
      x = sp_field.gf_inv(x);
      gf2m y = x;
      for(size_t j = 0; j < t; j++) {
         for(size_t k = 0; k < sp_field.get_extension_degree(); k++) {
            if(y & (1 << k)) {
               //the co-eff. are set in 2^0,...,2^11 ; 2^0,...,2^11 format along the rows/cols?
               H.set_coef_to_one(j * sp_field.get_extension_degree() + k, i);
            }
         }
         y = sp_field.gf_mul(y, lex_to_gray(L[i]));
      }
   }  //The H matrix is fed.

   secure_vector<size_t> perm = H.row_reduced_echelon_form();
   if(perm.empty()) {
      throw Invalid_State("McEliece keygen failed - could not bring matrix to row reduced echelon form");
   }

   auto result = std::make_unique<binary_matrix>(code_length - r, r);
   for(size_t i = 0; i < result->rows(); ++i) {
      for(size_t j = 0; j < result->columns(); ++j) {
         if(H.coef(j, perm[i])) {
            result->toggle_coeff(i, j);
         }
      }
   }

   std::vector<gf2m> Laux(code_length);
   for(size_t i = 0; i < code_length; ++i) {
      Laux[i] = L[perm[i]];
   }

   for(size_t i = 0; i < code_length; ++i) {
      L[i] = Laux[i];
   }
   return result;
}
}

McEliece_PrivateKey generate_mceliece_key(RandomNumberGenerator& rng, size_t ext_deg, size_t code_length, size_t t) {
   const size_t codimension = t * ext_deg;

   if(code_length <= codimension) {
      throw Invalid_Argument("invalid McEliece parameters");
   }

   auto sp_field = std::make_shared<GF2m_Field>(ext_deg);

   //pick the support.........
   std::vector<gf2m> L(code_length);

   for(size_t i = 0; i != L.size(); i++) {
      L[i] = static_cast<gf2m>(i);
   }
   randomize_support(L, rng);
   polyn_gf2m g(sp_field);  // create as zero

   bool success = false;
   std::unique_ptr<binary_matrix> R;

   do {
      // create a random irreducible polynomial
      g = polyn_gf2m(t, rng, sp_field);

      try {
         R = generate_R(L, &g, *sp_field, code_length, t);
         success = true;
      } catch(const Invalid_State&) {}
   } while(!success);

   std::vector<polyn_gf2m> sqrtmod = polyn_gf2m::sqrt_mod_init(g);
   std::vector<polyn_gf2m> F = syndrome_init(g, L, static_cast<int>(code_length));

   // Each F[i] is the (precomputed) syndrome of the error vector with
   // a single '1' in i-th position.
   // We do not store the F[i] as polynomials of degree t , but
   // as binary vectors of length ext_deg * t (this will
   // speed up the syndrome computation)
   //
   std::vector<uint32_t> H(bit_size_to_32bit_size(codimension) * code_length);
   uint32_t* sk = H.data();
   for(size_t i = 0; i < code_length; ++i) {
      for(size_t l = 0; l < t; ++l) {
         const size_t k = (l * ext_deg) / 32;
         const uint8_t j = (l * ext_deg) % 32;
         sk[k] ^= static_cast<uint32_t>(F[i].get_coef(l)) << j;
         if(j + ext_deg > 32) {
            sk[k + 1] ^= F[i].get_coef(l) >> (32 - j);
         }
      }
      sk += bit_size_to_32bit_size(codimension);
   }

   // We need the support L for decoding (decryption). In fact the
   // inverse is needed

   std::vector<gf2m> Linv(code_length);
   for(size_t i = 0; i != Linv.size(); ++i) {
      Linv[L[i]] = static_cast<gf2m>(i);
   }
   std::vector<uint8_t> pubmat(R->elem().size() * 4);
   for(size_t i = 0; i < R->elem().size(); i++) {
      store_le(R->elem()[i], &pubmat[i * 4]);
   }

   return McEliece_PrivateKey(g, H, sqrtmod, Linv, pubmat);
}

}

1	/*
2	* (C) Copyright Projet SECRET, INRIA, Rocquencourt
3	* (C) Bhaskar Biswas and Nicolas Sendrier
4	*
5	* (C) 2014 cryptosource GmbH
6	* (C) 2014 Falko Strenzke fstrenzke@cryptosource.de
7	* (C) 2015 Jack Lloyd
8	*
9	* Botan is released under the Simplified BSD License (see license.txt)
10	*
11	*/
12
13	#include <botan/mceliece.h>
14
15	#include <botan/internal/code_based_util.h>
16	#include <botan/internal/loadstor.h>
17	#include <botan/internal/mce_internal.h>
18	#include <botan/internal/polyn_gf2m.h>
19
20	namespace Botan {
21
22	namespace {
23
24	class binary_matrix final {	196✔
25	public:
26	binary_matrix(size_t m_rown, size_t m_coln);
27
28	void row_xor(size_t a, size_t b);
29	secure_vector<size_t> row_reduced_echelon_form();
30
31	/**
32	* return the coefficient out of F_2
33	*/
34	uint32_t coef(size_t i, size_t j) { return (m_elem[(i)*m_rwdcnt + (j) / 32] >> (j % 32)) & 1; }	99,912,736✔
35
36	void set_coef_to_one(size_t i, size_t j) {	65,136,545✔
37	m_elem[(i)*m_rwdcnt + (j) / 32] \|= (static_cast<uint32_t>(1) << ((j) % 32));	65,136,545✔
38	}	65,136,545✔
39
40	void toggle_coeff(size_t i, size_t j) {	49,958,370✔
41	m_elem[(i)*m_rwdcnt + (j) / 32] ^= (static_cast<uint32_t>(1) << ((j) % 32));	49,958,370✔
42	}	49,958,370✔
43
44	size_t rows() const { return m_rown; }	123,512✔
45
46	size_t columns() const { return m_coln; }	100,036,150✔
47
48	const std::vector<uint32_t>& elem() const { return m_elem; }	3,182,601✔
49
50	private:
51	size_t m_rown; // number of rows.
52	size_t m_coln; // number of columns.
53	size_t m_rwdcnt; // number of words in a row
54	std::vector<uint32_t> m_elem;
55	};
56
57	binary_matrix::binary_matrix(size_t rown, size_t coln) {	196✔
58	m_coln = coln;	196✔
59	m_rown = rown;	196✔
60	m_rwdcnt = 1 + ((m_coln - 1) / 32);	196✔
61	m_elem = std::vector<uint32_t>(m_rown * m_rwdcnt);	196✔
62	}	196✔
63
64	void binary_matrix::row_xor(size_t a, size_t b) {
65	for(size_t i = 0; i != m_rwdcnt; i++) {	2,349,803,369✔
66	m_elem[a * m_rwdcnt + i] ^= m_elem[b * m_rwdcnt + i];	2,334,625,715✔
67	}
68	}
69
70	//the matrix is reduced from LSB...(from right)
71	secure_vector<size_t> binary_matrix::row_reduced_echelon_form() {	98✔
72	secure_vector<size_t> perm(m_coln);	98✔
73	for(size_t i = 0; i != m_coln; i++) {	162,706✔
74	perm[i] = i; // initialize permutation.	162,608✔
75	}
76
77	uint32_t failcnt = 0;	98✔
78
79	size_t max = m_coln - 1;	98✔
80	for(size_t i = 0; i != m_rown; i++, max--) {	39,445✔
81	bool found_row = false;
82
83	for(size_t j = i; !found_row && j != m_rown; j++) {	117,726✔
84	if(coef(j, max)) {	78,379✔
85	if(i != j) //not needed as ith row is 0 and jth row is 1.	39,194✔
86	{
87	row_xor(i, j); //xor to the row.(swap)?	78,379✔
88	}
89
90	found_row = true;
91	}
92	}
93
94	//if no row with a 1 found then swap last column and the column with no 1 down.
95	if(!found_row) {	39,347✔
96	perm[m_coln - m_rown - 1 - failcnt] = static_cast<int>(max);	153✔
97	failcnt++;	153✔
98	if(!max) {	153✔
99	perm.clear();	×
100	}
101	i--;	153✔
102	} else {
103	perm[i + m_coln - m_rown] = max;	39,194✔
104	for(size_t j = i + 1; j < m_rown; j++) //fill the column downwards with 0's	15,199,501✔
105	{
106	if(coef(j, max)) {	15,160,307✔
107	row_xor(j, i); //check the arg. order.	15,160,307✔
108	}
109	}
110
111	//fill the column with 0's upwards too.
112	for(size_t j = i; j != 0; --j) {	15,199,501✔
113	if(coef(j - 1, max)) {	15,160,307✔
114	row_xor(j - 1, i);	15,199,501✔
115	}
116	}
117	}
118	} //end for(i)
119	return perm;	98✔
120	}
121
122	void randomize_support(std::vector<gf2m>& L, RandomNumberGenerator& rng) {	98✔
123	for(size_t i = 0; i != L.size(); ++i) {	162,706✔
124	gf2m rnd = random_gf2m(rng);	162,608✔
125
126	// no rejection sampling, but for useful code-based parameters with n <= 13 this seem tolerable
127	std::swap(L[i], L[rnd % L.size()]);	162,608✔
128	}
129	}	98✔
130
131	std::unique_ptr<binary_matrix> generate_R(	98✔
132	std::vector<gf2m>& L, polyn_gf2m* g, const GF2m_Field& sp_field, size_t code_length, size_t t) {
133	//L- Support
134	//t- Number of errors
135	//n- Length of the Goppa code
136	//m- The extension degree of the GF
137	//g- The generator polynomial.
138
139	const size_t r = t * sp_field.get_extension_degree();	98✔
140
141	binary_matrix H(r, code_length);	98✔
142
143	for(size_t i = 0; i != code_length; i++) {	162,706✔
144	gf2m x = g->eval(lex_to_gray(L[i])); //evaluate the polynomial at the point L[i].	162,608✔
145	x = sp_field.gf_inv(x);	162,608✔
146	gf2m y = x;
147	for(size_t j = 0; j < t; j++) {	10,755,904✔
148	for(size_t k = 0; k < sp_field.get_extension_degree(); k++) {	140,865,840✔
149	if(y & (1 << k)) {	130,272,544✔
150	//the co-eff. are set in 2^0,...,2^11 ; 2^0,...,2^11 format along the rows/cols?
151	H.set_coef_to_one(j * sp_field.get_extension_degree() + k, i);	130,272,544✔
152	}
153	}
154	y = sp_field.gf_mul(y, lex_to_gray(L[i]));	21,183,184✔
155	}
156	} //The H matrix is fed.
157
158	secure_vector<size_t> perm = H.row_reduced_echelon_form();	98✔
159	if(perm.empty()) {	98✔
160	throw Invalid_State("McEliece keygen failed - could not bring matrix to row reduced echelon form");	×
161	}
162
163	auto result = std::make_unique<binary_matrix>(code_length - r, r);	98✔
164	for(size_t i = 0; i < result->rows(); ++i) {	123,512✔
165	for(size_t j = 0; j < result->columns(); ++j) {	100,036,150✔
166	if(H.coef(j, perm[i])) {	99,912,736✔
167	result->toggle_coeff(i, j);	99,912,736✔
168	}
169	}
170	}
171
172	std::vector<gf2m> Laux(code_length);	98✔
173	for(size_t i = 0; i < code_length; ++i) {	162,706✔
174	Laux[i] = L[perm[i]];	162,608✔
175	}
176
177	for(size_t i = 0; i < code_length; ++i) {	162,706✔
178	L[i] = Laux[i];	162,608✔
179	}
180	return result;	98✔
181	}	294✔
182	}
183
184	McEliece_PrivateKey generate_mceliece_key(RandomNumberGenerator& rng, size_t ext_deg, size_t code_length, size_t t) {	98✔
185	const size_t codimension = t * ext_deg;	98✔
186
187	if(code_length <= codimension) {	98✔
188	throw Invalid_Argument("invalid McEliece parameters");	×
189	}
190
191	auto sp_field = std::make_shared<GF2m_Field>(ext_deg);	98✔
192
193	//pick the support.........
194	std::vector<gf2m> L(code_length);	98✔
195
196	for(size_t i = 0; i != L.size(); i++) {	162,706✔
197	L[i] = static_cast<gf2m>(i);	162,608✔
198	}
199	randomize_support(L, rng);	98✔
200	polyn_gf2m g(sp_field); // create as zero	98✔
201
202	bool success = false;	98✔
203	std::unique_ptr<binary_matrix> R;	98✔
204
205	do {	98✔
206	// create a random irreducible polynomial
207	g = polyn_gf2m(t, rng, sp_field);	196✔
208
209	try {	98✔
210	R = generate_R(L, &g, *sp_field, code_length, t);	98✔
211	success = true;	98✔
212	} catch(const Invalid_State&) {}	×
213	} while(!success);	98✔
214
215	std::vector<polyn_gf2m> sqrtmod = polyn_gf2m::sqrt_mod_init(g);	98✔
216	std::vector<polyn_gf2m> F = syndrome_init(g, L, static_cast<int>(code_length));	98✔
217
218	// Each F[i] is the (precomputed) syndrome of the error vector with
219	// a single '1' in i-th position.
220	// We do not store the F[i] as polynomials of degree t , but
221	// as binary vectors of length ext_deg * t (this will
222	// speed up the syndrome computation)
223	//
224	std::vector<uint32_t> H(bit_size_to_32bit_size(codimension) * code_length);	98✔
225	uint32_t* sk = H.data();	98✔
226	for(size_t i = 0; i < code_length; ++i) {	162,706✔
227	for(size_t l = 0; l < t; ++l) {	10,755,904✔
228	const size_t k = (l * ext_deg) / 32;	10,593,296✔
229	const uint8_t j = (l * ext_deg) % 32;	10,593,296✔
230	sk[k] ^= static_cast<uint32_t>(F[i].get_coef(l)) << j;	10,593,296✔
231	if(j + ext_deg > 32) {	10,593,296✔
232	sk[k + 1] ^= F[i].get_coef(l) >> (32 - j);	3,599,680✔
233	}
234	}
235	sk += bit_size_to_32bit_size(codimension);	162,608✔
236	}
237
238	// We need the support L for decoding (decryption). In fact the
239	// inverse is needed
240
241	std::vector<gf2m> Linv(code_length);	98✔
242	for(size_t i = 0; i != Linv.size(); ++i) {	162,706✔
243	Linv[L[i]] = static_cast<gf2m>(i);	162,608✔
244	}
245	std::vector<uint8_t> pubmat(R->elem().size() * 4);	98✔
246	for(size_t i = 0; i < R->elem().size(); i++) {	3,182,503✔
247	store_le(R->elem()[i], &pubmat[i * 4]);	3,182,405✔
248	}
249
250	return McEliece_PrivateKey(g, H, sqrtmod, Linv, pubmat);	98✔
251	}	490✔
252
253	}

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