| # Copyright 2021 Google LLC. |
| # |
| # Licensed under the Apache License, Version 2.0 (the "License"); |
| # you may not use this file except in compliance with the License. |
| # You may obtain a copy of the License at |
| # |
| # http://www.apache.org/licenses/LICENSE-2.0 |
| # |
| # Unless required by applicable law or agreed to in writing, software |
| # distributed under the License is distributed on an "AS IS" BASIS, |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
| # See the License for the specific language governing permissions and |
| # limitations under the License. |
| """Tests for pld_privacy_accountant.""" |
| |
| import math |
| |
| from absl.testing import absltest |
| from absl.testing import parameterized |
| |
| from dp_accounting import dp_event |
| from dp_accounting import privacy_accountant_test |
| from dp_accounting.pld import pld_privacy_accountant |
| |
| |
| class PldPrivacyAccountantTest(privacy_accountant_test.PrivacyAccountantTest, |
| parameterized.TestCase): |
| |
| def _make_test_accountants(self): |
| return [pld_privacy_accountant.PLDAccountant()] |
| |
| @parameterized.parameters( |
| dp_event.GaussianDpEvent(1.0), |
| dp_event.SelfComposedDpEvent(dp_event.GaussianDpEvent(1.0), 6), |
| dp_event.ComposedDpEvent( |
| [dp_event.GaussianDpEvent(1.0), |
| dp_event.GaussianDpEvent(2.0)]), |
| dp_event.PoissonSampledDpEvent(0.1, dp_event.GaussianDpEvent(1.0)), |
| dp_event.ComposedDpEvent([ |
| dp_event.PoissonSampledDpEvent(0.1, dp_event.GaussianDpEvent(1.0)), |
| dp_event.GaussianDpEvent(2.0) |
| ])) |
| def test_supports_gaussian(self, event): |
| pld_accountant = pld_privacy_accountant.PLDAccountant() |
| self.assertTrue(pld_accountant.supports(event)) |
| |
| @parameterized.parameters(0, -1) |
| def test_non_positive_composition_value_error(self, count): |
| event = dp_event.GaussianDpEvent(1.0) |
| accountant = pld_privacy_accountant.PLDAccountant() |
| with self.assertRaises(ValueError): |
| accountant.compose(event, count) |
| |
| @parameterized.parameters( |
| dp_event.GaussianDpEvent(0), |
| dp_event.LaplaceDpEvent(0), |
| dp_event.PoissonSampledDpEvent(0.1, dp_event.GaussianDpEvent(0)), |
| dp_event.PoissonSampledDpEvent(0.1, dp_event.LaplaceDpEvent(0))) |
| def test_additive_noise_mechanisms_with_zero_noise_multiplier(self, event): |
| accountant = pld_privacy_accountant.PLDAccountant() |
| accountant.compose(event) |
| self.assertEqual(accountant.get_delta(1.0), 1) |
| self.assertEqual(accountant.get_epsilon(0.01), math.inf) |
| |
| @parameterized.parameters( |
| dp_event.PoissonSampledDpEvent(0, dp_event.GaussianDpEvent(1)), |
| dp_event.PoissonSampledDpEvent(0, dp_event.LaplaceDpEvent(1)), |
| dp_event.PoissonSampledDpEvent(0, dp_event.GaussianDpEvent(0)), |
| dp_event.PoissonSampledDpEvent(0, dp_event.LaplaceDpEvent(0))) |
| def test_poisson_subsampling_with_zero_probability(self, event): |
| accountant = pld_privacy_accountant.PLDAccountant() |
| accountant.compose(event) |
| self.assertEqual(accountant.get_delta(0), 0) |
| self.assertEqual(accountant.get_epsilon(0), 0) |
| |
| def test_gaussian_basic(self): |
| gaussian_event = dp_event.GaussianDpEvent(noise_multiplier=math.sqrt(3)) |
| accountant = pld_privacy_accountant.PLDAccountant() |
| accountant.compose(gaussian_event, 1) |
| accountant.compose(gaussian_event, 2) |
| |
| exact_epsilon = 1 |
| exact_delta = 0.126936 |
| self.assertAlmostEqual( |
| accountant.get_delta(exact_epsilon), exact_delta, delta=1e-3) |
| self.assertAlmostEqual( |
| accountant.get_epsilon(exact_delta), exact_epsilon, delta=1e-3) |
| |
| def test_poisson_subsampled_gaussian(self): |
| subsampled_gaussian_event = dp_event.PoissonSampledDpEvent( |
| 0.2, dp_event.GaussianDpEvent(noise_multiplier=0.5)) |
| accountant = pld_privacy_accountant.PLDAccountant() |
| accountant.compose(subsampled_gaussian_event, 1) |
| accountant.compose(subsampled_gaussian_event, 2) |
| |
| exact_epsilon = 1 |
| expected_delta = 0.15594 |
| self.assertAlmostEqual( |
| accountant.get_delta(exact_epsilon), expected_delta, delta=1e-3) |
| self.assertAlmostEqual( |
| accountant.get_epsilon(expected_delta), exact_epsilon, delta=1e-3) |
| |
| def test_self_composed_subsampled_gaussian(self): |
| event = dp_event.SelfComposedDpEvent( |
| dp_event.PoissonSampledDpEvent(0.2, dp_event.GaussianDpEvent(0.5)), 3) |
| accountant = pld_privacy_accountant.PLDAccountant() |
| accountant.compose(event) |
| |
| exact_epsilon = 1 |
| expected_delta = 0.15594 |
| self.assertAlmostEqual( |
| accountant.get_delta(exact_epsilon), expected_delta, delta=1e-3) |
| self.assertAlmostEqual( |
| accountant.get_epsilon(expected_delta), exact_epsilon, delta=1e-3) |
| |
| def test_laplace_basic(self): |
| first_laplace_event = dp_event.LaplaceDpEvent(noise_multiplier=1) |
| second_laplace_event = dp_event.LaplaceDpEvent(noise_multiplier=2) |
| accountant = pld_privacy_accountant.PLDAccountant() |
| accountant.compose(first_laplace_event, 3) |
| accountant.compose(second_laplace_event, 2) |
| |
| expected_epsilon = 4 |
| expected_delta = 0 |
| self.assertAlmostEqual( |
| accountant.get_delta(expected_epsilon), expected_delta, delta=1e-6) |
| self.assertAlmostEqual( |
| accountant.get_epsilon(expected_delta), expected_epsilon, delta=1e-6) |
| |
| def test_poisson_subsampled_laplace(self): |
| subsampled_laplace_event = dp_event.PoissonSampledDpEvent( |
| 0.2, dp_event.LaplaceDpEvent(noise_multiplier=0.5)) |
| accountant = pld_privacy_accountant.PLDAccountant() |
| accountant.compose(subsampled_laplace_event, 1) |
| accountant.compose(subsampled_laplace_event, 2) |
| |
| exact_epsilon = 2.46964 |
| expected_delta = 0 |
| self.assertAlmostEqual( |
| accountant.get_delta(exact_epsilon), expected_delta, delta=1e-6) |
| self.assertAlmostEqual( |
| accountant.get_epsilon(expected_delta), exact_epsilon, delta=1e-3) |
| |
| |
| if __name__ == '__main__': |
| absltest.main() |