def test_deterministic_Prng_2_1(self):
prng = Prng(deterministic=True)
value = prng.getRandomLong(32)
expected = 3563776190
self.assertEqual(
value, expected, "Prng not deterministic (expected: %d, got: %d)" %
(expected, value))
def cipher(message, key):
prng = Prng(deterministic=True, seed=key)
randomString = prng.getRandomBytes(len(message))
stream = StringIO()
for i in range(len(message)):
stream.write(chr(ord(message[i])^ord(randomString[i])))
return stream.getvalue()
def test_deterministic_Prng_2_2(self):
prng = Prng(deterministic=True)
prng.addEntropy(b"entropy")
value = prng.getRandomLong(32)
expected = 1783747816
self.assertEqual(
value, expected, "Prng not deterministic (expected: %d, got: %d)" %
(expected, value))
def encode(self, share):
prng = Prng(deterministic=self.deterministic)
prng.addEntropy(share[0].to_bytes(512, byteorder="big") +
share[1].to_bytes(512, byteorder="big"))
result = b""
result += bytes([prng.getRandomLong(8) & 0xF0 | self.version])
result += bytes([prng.getRandomLong(8) & 0xF0 | self.k])
result += rawFromLong(share[0], 16)
result += rawFromLong(share[1], self.width)
return result
class TestPrng(unittest.TestCase):
""" tests for Prng.py"""
def setUp(self):
""" initial setup for testing. """
self.prng = Prng()
def testCreateRandomArray(self):
""" tests createRandomArray(self, rows, columns).
Check if the returned item:
(a) exists
(b) has the same dimensions as given
(c) is full of numbers (and not zeros, for example).
"""
self.arr = self.prng.createRandomArray(8, 10)
self.assertTrue(self.arr)
# check if rows are ok
self.assertEqual(len(self.arr), 8)
# then check if columns are ok, too
self.assertEqual([len(v) for k, v in self.arr.iteritems()][0], 10)
for k, v in self.arr.iteritems():
self.assertTrue(v)
def testGenHashAssistantNums(self):
""" tests if method returns a list full of random numbers
and of length(depth).
"""
self.row = self.prng.genHashAssistantNums(8)
self.assertTrue(self.row)
self.assertEqual(len(self.row), 8)
def testHash31(self):
""" tests if an item is hashed. """
self.number = 7798
self.result = self.prng.hash31(12344, 3456, self.number)
self.assertTrue(self.result)
def testBadArgumentHash31(self):
""" tests if hash31 can hash a non-numerical item. """
self.number = 'this is not a number'
self.result = self.prng.hash31(12344, 3456, self.number)
self.assertEqual(self.result, -1)
def testFourwise(self):
""" tests if fourwise works. """
self.result = self.prng.fourwise(213,45,6546,2342,43)
self.assertTrue(self.result)
def __init__(self, buckets=2000, depth=7):
""" 'Constructor' for ams sketch.
@param buckets the buckets for sketch
@param depth number of vectors.
"""
self.depth = depth
self.buckets = buckets
# following two lines are from imports (from the same package)
#self.operations = AMS_Operations(self.buckets, self.depth)
self.operation = AMS_Operations()
self.rand_gen = Prng()
self.count = 0
# create the sketch
#self.sketch = [[0 for i in range(self.buckets)] \
# for j in range(self.depth)]
self.sketch = [0 for i in range(self.buckets * self.depth)]
self.rand_array = self.createHelpArray()
class TestPrng(unittest.TestCase):
""" tests for Prng.py"""
def setUp(self):
""" initial setup for testing. """
self.prng = Prng()
def testCreateRandomArray(self):
""" tests createRandomArray(self, rows, columns).
Check if the returned item:
(a) exists
(b) has the same dimensions as given
(c) is full of numbers (and not zeros, for example).
"""
self.arr = self.prng.createRandomArray(8, 10)
self.assertTrue(self.arr)
# check if rows are ok
self.assertEqual(len(self.arr), 8)
# then check if columns are ok, too
self.assertEqual([len(v) for k, v in self.arr.iteritems()][0], 10)
for k, v in self.arr.iteritems():
self.assertTrue(v)
def testGenHashAssistantNums(self):
""" tests if method returns a list full of random numbers
and of length(depth).
"""
self.row = self.prng.genHashAssistantNums(8)
self.assertTrue(self.row)
self.assertEqual(len(self.row), 8)
def testHash31(self):
""" tests if an item is hashed. """
self.number = 7798
self.result = self.prng.hash31(12344, 3456, self.number)
self.assertTrue(self.result)
def testBadArgumentHash31(self):
""" tests if hash31 can hash a non-numerical item. """
self.number = 'this is not a number'
self.result = self.prng.hash31(12344, 3456, self.number)
self.assertEqual(self.result, -1)
def testFourwise(self):
""" tests if fourwise works. """
self.result = self.prng.fourwise(213, 45, 6546, 2342, 43)
self.assertTrue(self.result)
def split(self, secret, deterministic=True):
if secret >= self.P:
raise ValueError("secret must be smaller than P")
prng = Prng(deterministic=deterministic)
prng.addEntropy(secret.to_bytes(512, byteorder="big"))
a = [0] * self.k
a[0] = secret
for i in range(1, self.k):
a[i] = prng.getRandomLong(512) % self.P
x = []
for i in range(self.n):
while True:
r = int(prng.getRandomLong(16))
if r != 0 and r not in x:
x += [r]
break
y = [0] * self.n
for i in range(self.n):
y[i] = Shamir.fPoly(x[i], a, self.P)
shares = []
for i in range(self.n):
shares += [(x[i], y[i])]
return shares
def __init__(self, buckets=2000, depth=7):
""" 'Constructor' for ams sketch.
@param buckets the buckets for sketch
@param depth number of vectors.
"""
self.depth = depth
self.buckets = buckets
# following two lines are from imports (from the same package)
#self.operations = AMS_Operations(self.buckets, self.depth)
self.operation = AMS_Operations()
self.rand_gen = Prng()
self.count = 0
# create the sketch
#self.sketch = [[0 for i in range(self.buckets)] \
# for j in range(self.depth)]
self.sketch = [0 for i in range(self.buckets * self.depth)]
self.rand_array = self.createHelpArray()
def encrypt(plaintext, password):
nonce = Prng().getRandomBytes(32)
key = genkey.genKeyV3(password, nonce)
ciphertext = cipher(plaintext, key)
return MAGIC + utils.rawFromLong(VERSION, 16) + nonce + authenticate(ciphertext, key) + ciphertext
def setUp(self):
""" initial setup for testing. """
self.prng = Prng()
Use 5 D6 dice, loaded or not. After each roll of all dice:
- Put the dice in line without looking at them
- Starting from the right, remove any dice which have the same value as those on their left
- Roll the removed dice again
- Repeat until all dice have different values
- Enter the result and press return
eg:
5 1 2 1 5 : first throw
5 1 2 : remove 1 5 on the right
5 1 2 4 2 : second throw
5 1 2 4 : remove 2 on the right
5 1 2 4 6 : Ok! Enter 51246
""")
de = DiceEntropy()
prng = Prng()
while True:
print("roll: ")
roll = sys.stdin.readline()
de.addRoll(roll)
(dice_result, length) = de.getResult()
rand_value = prng.getRandomLong(length)
result = rand_value ^ dice_result
# print("rv={:x}".format(rand_value))
print("{} rolls, {} bits: {:x}".format(de.count_rolls, length, result))
results prettily and runs some more'''
from arduino import Arduino
from stattests import FipsTests
from time import time
from prng import Prng
from sys import argv, exit
if len(argv) < 2:
print "python all.py port"
exit(1)
port = argv[1]
ard = Arduino(port="/dev/ttyUSB" + port, debug=False, dbglevel=1500)
prng = Prng()
allalgs = [
prng.urandom, ard.vanilla, ard.leastsigrand, ard.meanrand, ard.updownrand,
ard.mixmeanupdown, ard.twoleastsignrand
]
#algs = [urandomtest, ard.vanilla, ard.leastsigrand]
#algs = [ard.updownrand, ard.mixmeanupdown, ard.twoleastsignrand]
#algs = [ard.twoleastsignrand, ard.updownrand]
algs = [ard.leastsigrand, ard.twoleastsignrand]
#algs = [prng.urandom, ard.vanilla, ard.leastsigrand, ard.twoleastsignrand, ard.meanrand]
k = 20000
def setUp(self):
""" initial setup for testing. """
self.prng = Prng()
def genKey(label, seed=None):
prng = Prng(deterministic=True, seed=seed)
prng.addEntropy(label.encode("utf-8"))
prng.skip(64 * 100000)
return prng.getRandomBytes(32)
class AMS_Sketch:
""" Implements an AMS-Sketch. """
def __init__(self, buckets=2000, depth=7):
""" 'Constructor' for ams sketch.
@param buckets the buckets for sketch
@param depth number of vectors.
"""
self.depth = depth
self.buckets = buckets
# following two lines are from imports (from the same package)
#self.operations = AMS_Operations(self.buckets, self.depth)
self.operation = AMS_Operations()
self.rand_gen = Prng()
self.count = 0
# create the sketch
#self.sketch = [[0 for i in range(self.buckets)] \
# for j in range(self.depth)]
self.sketch = [0 for i in range(self.buckets * self.depth)]
self.rand_array = self.createHelpArray()
def createHelpArray(self):
""" creates and returns an array full of random numbers.
Creates an array which contains random numbers that are used
for the production of pairwise independent hash functions.
@return the array.
"""
# first, be sure that everything that is needed, exists
assert self.buckets and self.depth and self.rand_gen
# create an array of (buckets x depth) length full of random
# integer numbers uniformly distributed in the range 0...2^31-1
self.rand_array = self.rand_gen.\
createRandomArray(self.buckets, self.depth)
return self.rand_array
def update(self, item, freq=1):
""" This method updates the sketch.
@param item is the new item that came.
@param freq is the frequency
"""
# work exactly the same way as in itemCount(item). Only last
# method changes.
self.offset = 0
for i in range(self.depth):
self.hash_value = self._compute_hash_value(item, i)
self.mult = self._compute_mult_factor(item, i)
self._update_sketch(self.mult, self.offset, self.hash_value, freq)
self.offset += self.buckets
def _update_sketch(self, mult, offset, hash_value, freq):
""" This method updates the sketch array. """
if (mult & 1) == 1:
self.sketch[offset + hash_value] += freq
else:
self.sketch[offset + hash_value] -= freq
def isCompatible(self, sketch):
""" compares the self.sketch with the argument sketch about
compatibility issues.
This method checks if the two sketches have the same dimensions
and the same help arrays and return true if sketches have the
same dimensions and help arrays and false otherwise.
@param sketch is a sketch.
@return boolean True if they are comparable or False otherwise.
"""
# check if all parameters of two sketches are the same.
# If they are, return True (that means that sketches can be
# added, substracted, etc).
if not sketch: return False
if self.buckets != sketch.buckets or \
self.depth != sketch.depth or \
self.rand_array != sketch.rand_array:
return False
return True
def itemCount(self, item):
""" returns the count of an item.
@param item is the item for which count is asked.
@return the count.
"""
self.offset = 0
self.estimates = [0] * self.depth
for i in range(self.depth):
# compute the hash value
self.hash_value = self._compute_hash_value(item, i)
# compute the mult factor (+/-1)
self.mult = self._compute_mult_factor(item, i)
# place the hash_value to the appropriate position
self._get_estimate(self.hash_value, self.mult, \
self.offset, self.estimates, i)
self.offset += self.buckets
# return the final estimate for item
self.count = self._compute_final_estimate(self.estimates)
return self.count
def _compute_hash_value(self, item, counter):
""" private method that computes the hash value. """
self.hash_value = self.rand_gen.hash31(\
self.rand_array[0][counter], self.rand_array[1][counter],\
item) % self.buckets
return self.hash_value
def _compute_mult_factor(self, item, counter):
""" private method that computes the mult factor. """
self.mult = self.rand_gen.fourwise(\
self.rand_array[0][counter], self.rand_array[1][counter], \
self.rand_array[2][counter], self.rand_array[3][counter], \
item)
return self.mult
def _get_estimate(self, hash_value, mult, offset, estimates, counter):
""" computes an estimate of the item. """
assert type(estimates) is list, "estimate not a list! (in ams.py)"
if mult & 1 == 1:
estimates[counter] += self.sketch[offset + hash_value]
else:
estimates[counter] -= self.sketch[offset + hash_value]
def _compute_final_estimate(self, estimates):
""" computes the final count (by the help of estimates) for
a particular item.
@param estimates the estimates for an item.
@return the count.
"""
# initialize count to -1 (it's an error value)
self.count = -1
# in the degenerated case depth==1: return the single estimate
if self.depth == 1:
self.count = estimates[0]
else:
# else, compute and return median. Full documentation is
# found here:
# http://docs.scipy.org/doc/numpy/reference/generated/numpy.median.html
self.count = numpy.median(estimates)
# make count integer before return it
return int(self.count)
def estimateF2(self, sketch):
""" estimates F2 moment of the vector (sum of squares).
@param sketch is the sketch.
@return the estimate F2.
"""
estimates = self._mult_by_pos(sketch, sketch)
self.result = self._compute_final_estimate(estimates)
return self.result
def _mult_by_pos(self, sketch_a, sketch_b):
""" This method computes the following formula:
r = sum(sketch_a[i] * sketch_b[i]), i in (1,...,N)
@param sketch_a is the first sketch (array)
@param sketch_b is the second sketch (array)
@return the inner product of (sketch_a, sketch_b)
"""
self.result = [a * b for a, b in zip(sketch_a, sketch_b)]
return self.result
def computeInnerProd(self, sketch):
""" computes inner product.
@param sketch is a sketch.
@return inner product
"""
# first of all, check if sketch is ok
self.isCompatible(sketch)
self.estimates = self._mult_by_pos(self.sketch, sketch)
self.result = self._compute_final_estimate(self.estimates)
return self.result
def setZero(self, sketch):
""" sets a whole sketch to contain zero values.
@param sketch is the sketch.
"""
assert type(self.sketch) is list, "not a list argument! setZero"
self.sketch = [0] * len(self.sketch)
class AMS_Sketch:
""" Implements an AMS-Sketch. """
def __init__(self, buckets=2000, depth=7):
""" 'Constructor' for ams sketch.
@param buckets the buckets for sketch
@param depth number of vectors.
"""
self.depth = depth
self.buckets = buckets
# following two lines are from imports (from the same package)
#self.operations = AMS_Operations(self.buckets, self.depth)
self.operation = AMS_Operations()
self.rand_gen = Prng()
self.count = 0
# create the sketch
#self.sketch = [[0 for i in range(self.buckets)] \
# for j in range(self.depth)]
self.sketch = [0 for i in range(self.buckets * self.depth)]
self.rand_array = self.createHelpArray()
def createHelpArray(self):
""" creates and returns an array full of random numbers.
Creates an array which contains random numbers that are used
for the production of pairwise independent hash functions.
@return the array.
"""
# first, be sure that everything that is needed, exists
assert self.buckets and self.depth and self.rand_gen
# create an array of (buckets x depth) length full of random
# integer numbers uniformly distributed in the range 0...2^31-1
self.rand_array = self.rand_gen.\
createRandomArray(self.buckets, self.depth)
return self.rand_array
def update(self, item, freq=1):
""" This method updates the sketch.
@param item is the new item that came.
@param freq is the frequency
"""
# work exactly the same way as in itemCount(item). Only last
# method changes.
self.offset = 0
for i in range(self.depth):
self.hash_value = self._compute_hash_value(item, i)
self.mult = self._compute_mult_factor(item, i)
self._update_sketch(self.mult, self.offset, self.hash_value, freq)
self.offset += self.buckets
def _update_sketch(self, mult, offset, hash_value, freq):
""" This method updates the sketch array. """
if (mult & 1) == 1:
self.sketch[offset + hash_value] += freq
else:
self.sketch[offset + hash_value] -= freq
def isCompatible(self, sketch):
""" compares the self.sketch with the argument sketch about
compatibility issues.
This method checks if the two sketches have the same dimensions
and the same help arrays and return true if sketches have the
same dimensions and help arrays and false otherwise.
@param sketch is a sketch.
@return boolean True if they are comparable or False otherwise.
"""
# check if all parameters of two sketches are the same.
# If they are, return True (that means that sketches can be
# added, substracted, etc).
if not sketch: return False
if self.buckets != sketch.buckets or \
self.depth != sketch.depth or \
self.rand_array != sketch.rand_array:
return False
return True
def itemCount(self, item):
""" returns the count of an item.
@param item is the item for which count is asked.
@return the count.
"""
self.offset = 0
self.estimates = [0] * self.depth
for i in range(self.depth):
# compute the hash value
self.hash_value = self._compute_hash_value(item, i)
# compute the mult factor (+/-1)
self.mult = self._compute_mult_factor(item, i)
# place the hash_value to the appropriate position
self._get_estimate(self.hash_value, self.mult, \
self.offset, self.estimates, i)
self.offset += self.buckets
# return the final estimate for item
self.count = self._compute_final_estimate(self.estimates)
return self.count
def _compute_hash_value(self, item, counter):
""" private method that computes the hash value. """
self.hash_value = self.rand_gen.hash31(\
self.rand_array[0][counter], self.rand_array[1][counter],\
item) % self.buckets
return self.hash_value
def _compute_mult_factor(self, item, counter):
""" private method that computes the mult factor. """
self.mult = self.rand_gen.fourwise(\
self.rand_array[0][counter], self.rand_array[1][counter], \
self.rand_array[2][counter], self.rand_array[3][counter], \
item)
return self.mult
def _get_estimate(self, hash_value, mult, offset, estimates, counter):
""" computes an estimate of the item. """
assert type(estimates) is list, "estimate not a list! (in ams.py)"
if mult & 1 == 1:
estimates[counter] += self.sketch[offset + hash_value]
else:
estimates[counter] -= self.sketch[offset + hash_value]
def _compute_final_estimate(self, estimates):
""" computes the final count (by the help of estimates) for
a particular item.
@param estimates the estimates for an item.
@return the count.
"""
# initialize count to -1 (it's an error value)
self.count = -1
# in the degenerated case depth==1: return the single estimate
if self.depth == 1:
self.count = estimates[0]
else:
# else, compute and return median. Full documentation is
# found here:
# http://docs.scipy.org/doc/numpy/reference/generated/numpy.median.html
self.count = numpy.median(estimates)
# make count integer before return it
return int(self.count)
def estimateF2(self, sketch):
""" estimates F2 moment of the vector (sum of squares).
@param sketch is the sketch.
@return the estimate F2.
"""
estimates = self._mult_by_pos(sketch, sketch)
self.result = self._compute_final_estimate(estimates)
return self.result
def _mult_by_pos(self, sketch_a, sketch_b):
""" This method computes the following formula:
r = sum(sketch_a[i] * sketch_b[i]), i in (1,...,N)
@param sketch_a is the first sketch (array)
@param sketch_b is the second sketch (array)
@return the inner product of (sketch_a, sketch_b)
"""
self.result = [a * b for a, b in zip(sketch_a, sketch_b)]
return self.result
def computeInnerProd(self, sketch):
""" computes inner product.
@param sketch is a sketch.
@return inner product
"""
# first of all, check if sketch is ok
self.isCompatible(sketch)
self.estimates = self._mult_by_pos(self.sketch, sketch)
self.result = self._compute_final_estimate(self.estimates)
return self.result
def setZero(self, sketch):
""" sets a whole sketch to contain zero values.
@param sketch is the sketch.
"""
assert type(self.sketch) is list, "not a list argument! setZero"
self.sketch = [0] * len(self.sketch)
