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Merge pull request #4985 from randombit/jack/fix-clang-tidy-cppcoreguidelines-prefer-member-initializer

Enable and fix clang-tidy warning cppcoreguidelines-prefer-member-initializer

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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_rown(rown), m_coln(coln), 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;
}
}  // namespace

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;

   // NOLINTNEXTLINE(*-avoid-do-while)
   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)
   //
   const size_t co32 = bit_size_to_32bit_size(codimension);
   std::vector<uint32_t> H(co32 * 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 size_t j = (l * ext_deg) % 32;
         sk[k] ^= static_cast<uint32_t>(F[i].get_coef(l)) << j;
         if(j + ext_deg > 32) {
            if(j > 0) {
               sk[k + 1] ^= F[i].get_coef(l) >> (32 - j);
            }
         }
      }
      sk += co32;
   }

   // 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);
}

}  // namespace Botan

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 {	186✔
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; }	83,361,466✔
35
36	void set_coef_to_one(size_t i, size_t j) {	54,281,368✔
37	m_elem[(i)*m_rwdcnt + (j) / 32] \|= (static_cast<uint32_t>(1) << ((j) % 32));	54,281,368✔
38	}	54,281,368✔
39
40	void toggle_coeff(size_t i, size_t j) {	41,679,325✔
41	m_elem[(i)*m_rwdcnt + (j) / 32] ^= (static_cast<uint32_t>(1) << ((j) % 32));	41,679,325✔
42	}	41,679,325✔
43
44	size_t rows() const { return m_rown; }	108,169✔
45
46	size_t columns() const { return m_coln; }	83,469,542✔
47
48	const std::vector<uint32_t>& elem() const { return m_elem; }	2,658,601✔
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) : m_rown(rown), m_coln(coln), m_rwdcnt(1 + ((m_coln - 1) / 32)) {	186✔
58	m_elem = std::vector<uint32_t>(m_rown * m_rwdcnt);	186✔
59	}	186✔
60
61	void binary_matrix::row_xor(size_t a, size_t b) {
62	for(size_t i = 0; i != m_rwdcnt; i++) {	1,938,004,500✔
63	m_elem[a * m_rwdcnt + i] ^= m_elem[b * m_rwdcnt + i];	1,925,405,719✔
64	}
65	}
66
67	//the matrix is reduced from LSB...(from right)
68	secure_vector<size_t> binary_matrix::row_reduced_echelon_form() {	93✔
69	secure_vector<size_t> perm(m_coln);	93✔
70	for(size_t i = 0; i != m_coln; i++) {	142,605✔
71	perm[i] = i; // initialize permutation.	142,512✔
72	}
73
74	uint32_t failcnt = 0;	93✔
75
76	size_t max = m_coln - 1;	93✔
77	for(size_t i = 0; i != m_rown; i++, max--) {	34,687✔
78	bool found_row = false;
79
80	for(size_t j = i; !found_row && j != m_rown; j++) {	103,695✔
81	if(coef(j, max)) {	69,101✔
82	if(i != j) //not needed as ith row is 0 and jth row is 1.	34,436✔
83	{
84	row_xor(i, j); //xor to the row.(swap)?	69,101✔
85	}
86
87	found_row = true;
88	}
89	}
90
91	//if no row with a 1 found then swap last column and the column with no 1 down.
92	if(!found_row) {	34,594✔
93	perm[m_coln - m_rown - 1 - failcnt] = static_cast<int>(max);	158✔
94	failcnt++;	158✔
95	if(!max) {	158✔
96	perm.clear();	×
97	}
98	i--;	158✔
99	} else {
100	perm[i + m_coln - m_rown] = max;	34,436✔
101	for(size_t j = i + 1; j < m_rown; j++) //fill the column downwards with 0's	12,614,061✔
102	{
103	if(coef(j, max)) {	12,579,625✔
104	row_xor(j, i); //check the arg. order.	12,579,625✔
105	}
106	}
107
108	//fill the column with 0's upwards too.
109	for(size_t j = i; j != 0; --j) {	12,614,061✔
110	if(coef(j - 1, max)) {	12,579,625✔
111	row_xor(j - 1, i);	12,614,061✔
112	}
113	}
114	}
115	} //end for(i)
116	return perm;	93✔
117	}
118
119	void randomize_support(std::vector<gf2m>& L, RandomNumberGenerator& rng) {	93✔
120	for(size_t i = 0; i != L.size(); ++i) {	142,605✔
121	gf2m rnd = random_gf2m(rng);	142,512✔
122
123	// no rejection sampling, but for useful code-based parameters with n <= 13 this seem tolerable
124	std::swap(L[i], L[rnd % L.size()]);	142,512✔
125	}
126	}	93✔
127
128	std::unique_ptr<binary_matrix> generate_R(	93✔
129	std::vector<gf2m>& L, polyn_gf2m* g, const GF2m_Field& sp_field, size_t code_length, size_t t) {
130	//L- Support
131	//t- Number of errors
132	//n- Length of the Goppa code
133	//m- The extension degree of the GF
134	//g- The generator polynomial.
135
136	const size_t r = t * sp_field.get_extension_degree();	93✔
137
138	binary_matrix H(r, code_length);	93✔
139
140	for(size_t i = 0; i != code_length; i++) {	142,605✔
141	gf2m x = g->eval(lex_to_gray(L[i])); //evaluate the polynomial at the point L[i].	142,512✔
142	x = sp_field.gf_inv(x);	142,512✔
143	gf2m y = x;
144	for(size_t j = 0; j < t; j++) {	9,026,112✔
145	for(size_t k = 0; k < sp_field.get_extension_degree(); k++) {	117,438,752✔
146	if(y & (1 << k)) {	108,555,152✔
147	//the co-eff. are set in 2^0,...,2^11 ; 2^0,...,2^11 format along the rows/cols?
148	H.set_coef_to_one(j * sp_field.get_extension_degree() + k, i);	54,281,368✔
149	}
150	}
151	y = sp_field.gf_mul(y, lex_to_gray(L[i]));	17,764,166✔
152	}
153	} //The H matrix is fed.
154
155	secure_vector<size_t> perm = H.row_reduced_echelon_form();	93✔
156	if(perm.empty()) {	93✔
157	throw Invalid_State("McEliece keygen failed - could not bring matrix to row reduced echelon form");	×
158	}
159
160	auto result = std::make_unique<binary_matrix>(code_length - r, r);	93✔
161	for(size_t i = 0; i < result->rows(); ++i) {	108,169✔
162	for(size_t j = 0; j < result->columns(); ++j) {	83,469,542✔
163	if(H.coef(j, perm[i])) {	83,361,466✔
164	result->toggle_coeff(i, j);	41,679,325✔
165	}
166	}
167	}
168
169	std::vector<gf2m> Laux(code_length);	93✔
170	for(size_t i = 0; i < code_length; ++i) {	142,605✔
171	Laux[i] = L[perm[i]];	142,512✔
172	}
173
174	for(size_t i = 0; i < code_length; ++i) {	142,605✔
175	L[i] = Laux[i];	142,512✔
176	}
177	return result;	93✔
178	}	279✔
179	} // namespace
180
181	McEliece_PrivateKey generate_mceliece_key(RandomNumberGenerator& rng, size_t ext_deg, size_t code_length, size_t t) {	93✔
182	const size_t codimension = t * ext_deg;	93✔
183
184	if(code_length <= codimension) {	93✔
185	throw Invalid_Argument("invalid McEliece parameters");	×
186	}
187
188	auto sp_field = std::make_shared<GF2m_Field>(ext_deg);	93✔
189
190	//pick the support.........
191	std::vector<gf2m> L(code_length);	93✔
192
193	for(size_t i = 0; i != L.size(); i++) {	142,605✔
194	L[i] = static_cast<gf2m>(i);	142,512✔
195	}
196	randomize_support(L, rng);	93✔
197	polyn_gf2m g(sp_field); // create as zero	93✔
198
199	bool success = false;	93✔
200	std::unique_ptr<binary_matrix> R;	93✔
201
202	// NOLINTNEXTLINE(*-avoid-do-while)
203	do {	93✔
204	// create a random irreducible polynomial
205	g = polyn_gf2m(t, rng, sp_field);	186✔
206
207	try {	93✔
208	R = generate_R(L, &g, *sp_field, code_length, t);	186✔
209	success = true;	93✔
210	} catch(const Invalid_State&) {}	×
211	} while(!success);	93✔
212
213	std::vector<polyn_gf2m> sqrtmod = polyn_gf2m::sqrt_mod_init(g);	93✔
214	std::vector<polyn_gf2m> F = syndrome_init(g, L, static_cast<int>(code_length));	93✔
215
216	// Each F[i] is the (precomputed) syndrome of the error vector with
217	// a single '1' in i-th position.
218	// We do not store the F[i] as polynomials of degree t , but
219	// as binary vectors of length ext_deg * t (this will
220	// speed up the syndrome computation)
221	//
222	const size_t co32 = bit_size_to_32bit_size(codimension);	93✔
223	std::vector<uint32_t> H(co32 * code_length);	93✔
224	uint32_t* sk = H.data();	93✔
225	for(size_t i = 0; i < code_length; ++i) {	142,605✔
226	for(size_t l = 0; l < t; ++l) {	9,026,112✔
227	const size_t k = (l * ext_deg) / 32;	8,883,600✔
228	const size_t j = (l * ext_deg) % 32;	8,883,600✔
229	sk[k] ^= static_cast<uint32_t>(F[i].get_coef(l)) << j;	8,883,600✔
230	if(j + ext_deg > 32) {	8,883,600✔
231	if(j > 0) {	3,021,728✔
232	sk[k + 1] ^= F[i].get_coef(l) >> (32 - j);	3,021,728✔
233	}
234	}
235	}
236	sk += co32;	142,512✔
237	}
238
239	// We need the support L for decoding (decryption). In fact the
240	// inverse is needed
241
242	std::vector<gf2m> Linv(code_length);	93✔
243	for(size_t i = 0; i != Linv.size(); ++i) {	142,605✔
244	Linv[L[i]] = static_cast<gf2m>(i);	142,512✔
245	}
246	std::vector<uint8_t> pubmat(R->elem().size() * 4);	93✔
247	for(size_t i = 0; i < R->elem().size(); i++) {	2,658,508✔
248	store_le(R->elem()[i], &pubmat[i * 4]);	2,658,415✔
249	}
250
251	return McEliece_PrivateKey(g, H, sqrtmod, Linv, pubmat);	93✔
252	}	465✔
253
254	} // namespace Botan

randombit / botan / 16250409945

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