def test_dp_sum_structure_complex(self):
query = tensorflow_privacy.GaussianSumQuery(5.0, 0.0)
def datapoint(a, b, c):
return collections.OrderedDict(a=(a, ), bc=([b], (c, )))
data = [
datapoint(1.0, 2.0, 1.0),
datapoint(2.0, 3.0, 1.0),
datapoint(6.0, 8.0, 0.0), # Clipped to 3.0, 4.0, 0.0
]
value_type = type_conversions.type_from_tensors(data[0])
dp_aggregate_process = differential_privacy.build_dp_aggregate_process(
value_type, query)
global_state = dp_aggregate_process.initialize()
output = dp_aggregate_process.next(global_state, data, [1.0, 1.0, 1.0])
self.assertEqual(output.state.l2_norm_clip, 5.0)
self.assertEqual(output.state.stddev, 0.0)
self.assertEqual(output.result['a'][0], 6.0)
self.assertEqual(output.result['bc'][0][0], 9.0)
self.assertEqual(output.result['bc'][1][0], 2.0)
def test_dp_stateful_mean(self):
class ShrinkingSumQuery(tensorflow_privacy.GaussianSumQuery):
def get_noised_result(self, sample_state, global_state):
global_state = self._GlobalState(
tf.maximum(global_state.l2_norm_clip - 1, 0.0),
global_state.stddev)
return sample_state, global_state
query = ShrinkingSumQuery(4.0, 0.0)
value_type = type_conversions.type_from_tensors(0.0)
dp_aggregate_process = differential_privacy.build_dp_aggregate_process(
value_type, query)
global_state = dp_aggregate_process.initialize()
records = [1.0, 3.0, 5.0]
def run_and_check(global_state, expected_l2_norm_clip,
expected_result):
output = dp_aggregate_process.next(global_state, records,
[1.0, 1.0, 1.0])
self.assertEqual(output.state.l2_norm_clip, expected_l2_norm_clip)
self.assertEqual(output.result, expected_result)
return output.state
self.assertEqual(global_state.l2_norm_clip, 4.0)
global_state = run_and_check(global_state, 3.0, 8.0)
global_state = run_and_check(global_state, 2.0, 7.0)
global_state = run_and_check(global_state, 1.0, 5.0)
global_state = run_and_check(global_state, 0.0, 3.0)
global_state = run_and_check(global_state, 0.0, 0.0)
def test_dp_sum_structure_list(self):
query = tensorflow_privacy.GaussianSumQuery(5.0, 0.0)
def datapoint(a, b):
return [tf.Variable(a, name='a'), tf.Variable(b, name='b')]
data = [
datapoint(1.0, 2.0),
datapoint(2.0, 3.0),
datapoint(6.0, 8.0), # Clipped to 3.0, 4.0
]
value_type = type_conversions.type_from_tensors(data[0])
dp_aggregate_process = differential_privacy.build_dp_aggregate_process(
value_type, query)
global_state = dp_aggregate_process.initialize()
output = dp_aggregate_process.next(global_state, data, [1.0, 1.0, 1.0])
self.assertEqual(output.state.l2_norm_clip, 5.0)
self.assertEqual(output.state.stddev, 0.0)
result = list(output.result)
self.assertEqual(result[0], 6.0)
self.assertEqual(result[1], 9.0)
def test_dp_sum_structure_nested_odict(self):
query = tensorflow_privacy.GaussianSumQuery(5.0, 0.0)
def datapoint(a, b, c):
return collections.OrderedDict([('a', (a,)),
('bc',
collections.OrderedDict([('b', [b]),
('c', (c,))]))])
data = [
datapoint(1.0, 2.0, 1.0),
datapoint(2.0, 3.0, 1.0),
datapoint(6.0, 8.0, 0.0), # Clipped to 3.0, 4.0, 0.0
]
value_type = type_conversions.type_from_tensors(data[0])
dp_aggregate_process = differential_privacy.build_dp_aggregate_process(
value_type, query)
global_state = dp_aggregate_process.initialize()
output = dp_aggregate_process.next(global_state, data, [1.0, 1.0, 1.0])
self.assertEqual(output['state']['l2_norm_clip'], 5.0)
self.assertEqual(output['state']['stddev'], 0.0)
self.assertEqual(output['result']['a'][0], 6.0)
self.assertEqual(output['result']['bc']['b'][0], 9.0)
self.assertEqual(output['result']['bc']['c'][0], 2.0)
def test_process_type_signature(self, value_template):
query = tensorflow_privacy.GaussianSumQuery(4.0, 0.0)
value_type = type_conversions.type_from_tensors(value_template)
dp_aggregate_process = differential_privacy.build_dp_aggregate_process(
value_type, query)
server_state_type = computation_types.FederatedType(
computation_types.NamedTupleType([('l2_norm_clip', tf.float32),
('stddev', tf.float32)]),
placements.SERVER)
self.assertEqual(
dp_aggregate_process.initialize.type_signature,
computation_types.FunctionType(
parameter=None, result=server_state_type))
client_value_type = computation_types.FederatedType(value_type,
placements.CLIENTS)
client_value_weight_type = computation_types.FederatedType(
tf.float32, placements.CLIENTS)
server_result_type = computation_types.FederatedType(
value_type, placements.SERVER)
server_metrics_type = computation_types.FederatedType((), placements.SERVER)
self.assertEqual(
dp_aggregate_process.next.type_signature,
computation_types.FunctionType(
parameter=computation_types.NamedTupleType([
(None, server_state_type), (None, client_value_type),
(None, client_value_weight_type)
]),
result=computation_types.NamedTupleType([
('state', server_state_type), ('result', server_result_type),
('measurements', server_metrics_type)
])))
def test_process_type_signature(self, value_template):
query = tensorflow_privacy.GaussianSumQuery(4.0, 0.0)
value_type = type_conversions.type_from_tensors(value_template)
dp_aggregate_process = differential_privacy.build_dp_aggregate_process(
value_type, query)
server_state_type = computation_types.FederatedType(
type_conversions.type_from_tensors(query.initial_global_state()),
placements.SERVER)
self.assertEqual(
dp_aggregate_process.initialize.type_signature,
computation_types.FunctionType(parameter=None,
result=server_state_type))
client_value_type = computation_types.FederatedType(
value_type, placements.CLIENTS)
client_value_weight_type = computation_types.FederatedType(
tf.float32, placements.CLIENTS)
server_result_type = computation_types.FederatedType(
value_type, placements.SERVER)
server_metrics_type = computation_types.FederatedType(
(), placements.SERVER)
self.assertEqual(
dp_aggregate_process.next.type_signature,
computation_types.FunctionType(
parameter=(server_state_type, client_value_type,
client_value_weight_type),
result=collections.OrderedDict(
state=server_state_type,
result=server_result_type,
measurements=server_metrics_type)))
def test_dp_sum(self):
query = tensorflow_privacy.GaussianSumQuery(4.0, 0.0)
value_type = type_conversions.type_from_tensors(0.0)
dp_aggregate_process = differential_privacy.build_dp_aggregate_process(
value_type, query)
global_state = dp_aggregate_process.initialize()
output = dp_aggregate_process.next(global_state, [1.0, 3.0, 5.0],
[1.0, 1.0, 1.0])
self.assertEqual(output.state.l2_norm_clip, 4.0)
self.assertEqual(output.state.stddev, 0.0)
self.assertEqual(output.result, 8.0)
