def test_self_convolve_dictionary(self):
inp_dictionary = {1: 2, 3: 5, 4: 6}
expected_result = {
3: 8,
5: 60,
6: 72,
7: 150,
8: 360,
9: 341,
10: 450,
11: 540,
12: 216
}
result = common.self_convolve_dictionary(inp_dictionary, 3)
test_util.assert_dictionary_almost_equal(self, expected_result, result)
def test_discrete_laplace_privacy_loss_tail(
self, parameter, sensitivity, sampling_prob, adjacency_type,
expected_lower_x_truncation, expected_upper_x_truncation,
expected_tail_probability_mass_function):
pl = privacy_loss_mechanism.DiscreteLaplacePrivacyLoss(
parameter,
sensitivity=sensitivity,
sampling_prob=sampling_prob,
adjacency_type=adjacency_type)
tail_pld = pl.privacy_loss_tail()
self.assertAlmostEqual(expected_lower_x_truncation,
tail_pld.lower_x_truncation)
self.assertAlmostEqual(expected_upper_x_truncation,
tail_pld.upper_x_truncation)
test_util.assert_dictionary_almost_equal(
self, expected_tail_probability_mass_function,
tail_pld.tail_probability_mass_function)
def test_discrete_gaussian_privacy_loss_tail(
self, sigma, sensitivity, truncation_bound, sampling_prob,
adjacency_type, expected_lower_x_truncation,
expected_upper_x_truncation,
expected_tail_probability_mass_function):
pl = privacy_loss_mechanism.DiscreteGaussianPrivacyLoss(
sigma,
sensitivity=sensitivity,
truncation_bound=truncation_bound,
sampling_prob=sampling_prob,
adjacency_type=adjacency_type)
tail_pld = pl.privacy_loss_tail()
self.assertAlmostEqual(expected_lower_x_truncation,
tail_pld.lower_x_truncation)
self.assertAlmostEqual(expected_upper_x_truncation,
tail_pld.upper_x_truncation)
test_util.assert_dictionary_almost_equal(
self, expected_tail_probability_mass_function,
tail_pld.tail_probability_mass_function)
def test_gaussian_privacy_loss_tail(
self, standard_deviation, sensitivity, sampling_prob,
adjacency_type, expected_lower_x_truncation,
expected_upper_x_truncation, pessimistic_estimate,
expected_tail_probability_mass_function):
pl = privacy_loss_mechanism.GaussianPrivacyLoss(
standard_deviation,
sensitivity=sensitivity,
pessimistic_estimate=pessimistic_estimate,
log_mass_truncation_bound=math.log(2) + stats.norm.logcdf(-1),
sampling_prob=sampling_prob,
adjacency_type=adjacency_type)
tail_pld = pl.privacy_loss_tail()
self.assertAlmostEqual(expected_lower_x_truncation,
tail_pld.lower_x_truncation)
self.assertAlmostEqual(expected_upper_x_truncation,
tail_pld.upper_x_truncation)
test_util.assert_dictionary_almost_equal(
self, expected_tail_probability_mass_function,
tail_pld.tail_probability_mass_function)
def test_convolve_dictionary_with_truncation(self):
dictionary1 = {1: 0.4, 2: 0.6}
dictionary2 = {1: 0.7, 3: 0.3}
expected_result = {3: 0.42, 4: 0.12}
result = common.convolve_dictionary(dictionary1, dictionary2, 0.57)
test_util.assert_dictionary_almost_equal(self, expected_result, result)
def test_convolve_dictionary(self):
dictionary1 = {1: 2, 3: 4}
dictionary2 = {2: 3, 4: 6}
expected_result = {3: 6, 5: 24, 7: 24}
result = common.convolve_dictionary(dictionary1, dictionary2)
test_util.assert_dictionary_almost_equal(self, expected_result, result)
def test_list_to_dict_truncation(self, input_list, offset,
tail_mass_truncation, expected_result):
result = common.list_to_dictionary(
input_list, offset, tail_mass_truncation=tail_mass_truncation)
test_util.assert_dictionary_almost_equal(self, expected_result, result)
def test_dictionary_almost_equal(self, dict1, dict2, expected_result):
if expected_result:
test_util.assert_dictionary_almost_equal(self, dict1, dict2)
else:
with self.assertRaises(AssertionError):
test_util.assert_dictionary_almost_equal(self, dict1, dict2)