def test_secure_lightweight(self):
test_array = array([
[1, -1, -1, 1, -1, 1],
[-1, 1, 1, -1, -1, 1],
])
assert_array_equal(
LTFArray.transform_lightweight_secure(test_array, k=3), [
[
[-1, -1, -1, 1, 1, -1],
[-1, -1, 1, 1, -1, -1],
[-1, 1, 1, -1, -1, -1],
],
[
[-1, -1, -1, -1, 1, 1],
[-1, -1, -1, 1, 1, -1],
[-1, -1, 1, 1, -1, -1],
],
])
def test_secure_lightweight(self):
"""This method tests the secure lightweight transformation with predefined input and output."""
test_array = array([
[1, -1, -1, 1, -1, 1],
[-1, 1, 1, -1, -1, 1],
],
dtype=tools.BIT_TYPE)
assert_array_equal(
LTFArray.transform_lightweight_secure(test_array, k=3), [
[
[1, -1, 1, -1, -1, -1],
[1, 1, -1, -1, 1, -1],
[-1, 1, -1, 1, -1, 1],
],
[
[1, -1, 1, -1, 1, 1],
[-1, -1, -1, 1, 1, -1],
[1, -1, 1, -1, -1, -1],
],
])
def __init__(self,
n,
k,
training_set,
validation_set,
weights_mu=0,
weights_sigma=1,
weights_prng=RandomState(),
lr_iteration_limit=1000,
mini_batch_size=0,
convergence_decimals=2,
shuffle=False,
logger=None):
"""
Initialize a Correlation Attack Learner for the specified LTF Array which uses transform_lightweight_secure.
:param n: Input length
:param k: Number of parallel LTFs in the LTF Array
:param training_set: The training set, i.e. a data structure containing challenge response pairs
:param validation_set: The validation set, i.e. a data structure containing challenge response pairs. Used for
approximating accuracies of permuted models (can be smaller e.g. 0.1*training_set_size)
:param weights_mu: mean of the Gaussian that is used to choose the initial model
:param weights_sigma: standard deviation of the Gaussian that is used to choose the initial model
:param weights_prng: PRNG to draw the initial model from. Defaults to fresh `numpy.random.RandomState` instance.
:param lr_iteration_limit: Iteration limit for a single LR learner run
:param logger: logging.Logger
Logger which is used to log detailed information of learn iterations.
"""
self.n = n
self.k = k
self.validation_set_efba = ChallengeResponseSet(
challenges=LTFArray.efba_bit(
LTFArray.transform_lightweight_secure(
validation_set.challenges, k)),
responses=validation_set.responses)
self.logger = logger
self.lr_learner = LogisticRegression(
t_set=training_set,
n=n,
k=k,
transformation=LTFArray.transform_lightweight_secure,
combiner=LTFArray.combiner_xor,
weights_mu=weights_mu,
weights_sigma=weights_sigma,
weights_prng=weights_prng,
logger=logger,
iteration_limit=lr_iteration_limit,
minibatch_size=mini_batch_size,
convergence_decimals=convergence_decimals,
shuffle=shuffle)
self.initial_accuracy = .5
self.initial_lr_iterations = 0
self.initial_model = None
self.total_lr_iterations = 0
self.best_permutation_iteration = 0
self.total_permutation_iterations = 0
self.best_permutation = None
self.best_accuracy = None
assert n in (
64, 128
), 'Correlation attack for %i bit is currently not supported.' % n
assert validation_set.N >= 1000, 'Validation set should contain at least 1000 challenges.'
self.correlation_permutations = loadmat(
'data/correlation_permutations_lightweight_secure_%i_10.mat' %
n)['shiftOverviewData'][:, :, 0].astype('int64')