def test_laplace_from_privacy_parameters(self, sensitivity, epsilon, delta,
expected_parameter):
pld = privacy_loss_distribution.LaplacePrivacyLossDistribution.from_privacy_guarantee(
privacy_loss_distribution.DifferentialPrivacyParameters(epsilon, delta),
sensitivity,
value_discretization_interval=1)
self.assertAlmostEqual(expected_parameter, pld._parameter)
def test_gaussian_from_privacy_parameters(self, sensitivity, epsilon, delta,
expected_standard_deviation):
pld = privacy_loss_distribution.GaussianPrivacyLossDistribution.from_privacy_guarantee(
privacy_loss_distribution.DifferentialPrivacyParameters(epsilon, delta),
sensitivity,
value_discretization_interval=1)
self.assertAlmostEqual(expected_standard_deviation, pld._standard_deviation,
3)
def test_from_privacy_parameters(self, epsilon, delta,
value_discretization_interval,
expected_rounded_probability_mass_function,
expected_infinity_mass):
pld = privacy_loss_distribution.PrivacyLossDistribution.from_privacy_parameters(
privacy_loss_distribution.DifferentialPrivacyParameters(epsilon, delta),
value_discretization_interval=value_discretization_interval)
self.assertAlmostEqual(expected_infinity_mass, pld.infinity_mass)
dictionary_almost_equal(self, expected_rounded_probability_mass_function,
pld.rounded_probability_mass_function)
