def test_build_dp_query_adaptive(self):
ccba = 0.1
query = differential_privacy.build_dp_query(
1.0,
2.0,
3.0,
adaptive_clip_learning_rate=0.05,
target_unclipped_quantile=0.5,
clipped_count_budget_allocation=ccba,
expected_num_clients=10)
self.assertIsInstance(query, privacy.QuantileAdaptiveClipAverageQuery)
self.assertEqual(query._numerator._initial_l2_norm_clip, 1.0)
expected_sum_query_noise_multiplier = 2.0 * (1.0 - ccba)**(-0.5)
self.assertAlmostEqual(query._numerator._noise_multiplier,
expected_sum_query_noise_multiplier)
self.assertEqual(query._numerator._target_unclipped_quantile, 0.5)
self.assertEqual(query._numerator._learning_rate, 0.05)
self.assertEqual(
query._numerator._clipped_fraction_query._numerator._l2_norm_clip,
0.5)
expected_clipped_count_stddev = 0.5 * 2.0 * ccba**(-0.5)
self.assertAlmostEqual(
query._numerator._clipped_fraction_query._numerator._stddev,
expected_clipped_count_stddev)
self.assertEqual(query._numerator._clipped_fraction_query._denominator,
10)
self.assertEqual(query._denominator, 3.0)
def test_build_dp_query_basic(self):
query = differential_privacy.build_dp_query(1.0, 2.0, 3.0)
self.assertIsInstance(query, privacy.GaussianAverageQuery)
self.assertEqual(query._numerator._l2_norm_clip, 1.0)
self.assertEqual(query._numerator._stddev, 2.0)
self.assertEqual(query._denominator, 3.0)
def test_build_dp_query_adaptive(self):
ccba = 0.1
query = differential_privacy.build_dp_query(
1.0,
2.0,
3.0,
adaptive_clip_learning_rate=0.05,
target_unclipped_quantile=0.5,
clipped_count_budget_allocation=ccba,
expected_clients_per_round=10)
self.assertIsInstance(
query, tensorflow_privacy.QuantileAdaptiveClipAverageQuery)
self.assertIsInstance(query._numerator,
tensorflow_privacy.QuantileAdaptiveClipSumQuery)
expected_sum_query_noise_multiplier = 2.0 * (1.0 - ccba)**(-0.5)
self.assertAlmostEqual(query._numerator._noise_multiplier,
expected_sum_query_noise_multiplier)
self.assertEqual(query._denominator, 3.0)
def test_build_dp_query_per_vector(self):
class MockTensor():
def __init__(self, shape):
self.shape = shape
mock_shape = collections.namedtuple('MockShape', ['dims'])
mock_dim = collections.namedtuple('MockDim', ['value'])
mock_model = collections.namedtuple('MockModel', ['weights'])
mock_weights = collections.namedtuple('MockWeights', ['trainable'])
def make_mock_tensor(*dims):
return MockTensor(mock_shape([mock_dim(dim) for dim in dims]))
vectors = collections.OrderedDict(a=make_mock_tensor(2),
b=make_mock_tensor(2, 3),
c=make_mock_tensor(1, 3, 4))
model = mock_model(mock_weights(vectors))
query = differential_privacy.build_dp_query(1.0,
2.0,
3.0,
per_vector_clipping=True,
model=model)
self.assertIsInstance(query, tensorflow_privacy.NestedQuery)
def check(subquery):
self.assertIsInstance(subquery,
tensorflow_privacy.GaussianAverageQuery)
self.assertEqual(subquery._denominator, 3.0)
tf.nest.map_structure(check, query._queries)
noise_multipliers = tf.nest.flatten(
tf.nest.map_structure(
lambda query: query._numerator._stddev / query._numerator.
_l2_norm_clip, query._queries))
effective_noise_multiplier = sum([x**-2.0
for x in noise_multipliers])**-0.5
self.assertAlmostEqual(effective_noise_multiplier, 2.0)
