def random_involution(n):
print 'WARNING: too low number of fixed points! not a uniform distribution'
pairs = range(2**n)
shuffle(pairs)
s = [0] * 2**n
while pairs:
if len(pairs) == 1 or randint(1, len(pairs)) == 1:
a = b = pairs.pop()
else:
a, b = pairs.pop(), pairs.pop()
s[a] = b
s[b] = a
return s
def ddt_max_estimation(self, iter=100, limit=None):
mx = 0
for i in xrange(iter):
dx = randint(1, self.insize - 1)
cnt = 0
dys = Counter()
for x in xrange(self.insize):
dy = self[x] ^ self[x ^ dx]
dys[dy] += 1
if limit is not None and dys[dy] > limit:
return dys[dy]
mx = max(mx, dys[dy])
return mx
def random_sbox_of_degree(m, n, d, h**o=False, force_all_maxterms=False):
anfs = [list() for i in xrange(n)]
for out_bit in xrange(n):
mindeg = 1 if h**o else 0
for deg in xrange(mindeg, d + 1):
for mask in hamming_masks(m, deg):
if randint(0, 1) == 1 or (force_all_maxterms and deg == d):
anfs[out_bit].append(mask)
res = []
for x in xrange(2**m):
y = 0
for e, anf in enumerate(anfs):
e = n - 1 - e
val = 0
for mask in anf:
val ^= ((mask & x) == mask)
val &= 1
y |= val << e
res.append(y)
return res
def random_affine(*args):
lin = random_linear(*args)
xor = randint(0, max(lin))
return [y ^ xor for y in lin]
def random_affine_permutation(n):
xor = randint(0, 2**n-1)
return [y ^ xor for y in from_matrix(random_invertible_matrix(n))]
def random_Boolean_function_of_degree(m, d):
anf = [randint(0, 1) if hw(x) <= d else 0 for x in xrange(2**m)]
return gen.SBox2(anf, n=1).mobius()
def random_function(m, n=None, h**o=False):
n = n or m
res = [randint(0, 2**n - 1) for i in xrange(2**m)]
if h**o:
res[0] = 0
return res
def randomize_xor(self):
a = randint(0, self.insize - 1)
b = randint(0, self.outsize - 1)
return self.xor(a, b)
