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General cleanup.

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1. Rename PrivateQuery to DPQuery.
2. Move construction of DPQuery to outside of optimizer.
3. Remove PrivateAverageQuery and PrivateSumQuery, and rename DPQuery's 'get_query_result' method to 'get_noised_result'. Rename private_queries.py to dp_query.py.
4. Remove thrice-replicated run_query function from the test classes and replace with a single function in new test_utils.py.
5. Add functions gaussian_sum_query_from_noise_multplier and gaussian_average_query_from_noise_multplier.

PiperOrigin-RevId: 230595991
This commit is contained in:
Galen Andrew 2019-01-23 13:51:58 -08:00 committed by schien1729
parent 7e2d796bde
commit c8cb3c6b70
11 changed files with 189 additions and 185 deletions
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52
privacy/optimizers/dp_optimizer.py
View file

@ -37,22 +37,19 @@ def make_optimizer_class(cls):
def __init__(
self,
l2_norm_clip,
noise_multiplier,
dp_average_query,
num_microbatches,
unroll_microbatches=False,
*args, # pylint: disable=keyword-arg-before-vararg
**kwargs):
super(DPOptimizerClass, self).__init__(*args, **kwargs)
stddev = l2_norm_clip * noise_multiplier
self._dp_average_query = dp_average_query
self._num_microbatches = num_microbatches
self._private_query = gaussian_query.GaussianAverageQuery(
l2_norm_clip, stddev, num_microbatches)
self._global_state = self._dp_average_query.initial_global_state()
# TODO(b/122613513): Set unroll_microbatches=True to avoid this bug.
# Beware: When num_microbatches is large (>100), enabling this parameter
# may cause an OOM error.
self._unroll_microbatches = unroll_microbatches
self._global_state = self._private_query.initial_global_state()
def compute_gradients(self,
loss,
@ -68,7 +65,7 @@ def make_optimizer_class(cls):
# sampling from the dataset without replacement.
microbatches_losses = tf.reshape(loss, [self._num_microbatches, -1])
sample_params = (
self._private_query.derive_sample_params(self._global_state))
self._dp_average_query.derive_sample_params(self._global_state))
def process_microbatch(i, sample_state):
"""Process one microbatch (record) with privacy helper."""
@ -76,7 +73,7 @@ def make_optimizer_class(cls):
tf.gather(microbatches_losses, [i]), var_list, gate_gradients,
aggregation_method, colocate_gradients_with_ops, grad_loss))
grads_list = list(grads)
sample_state = self._private_query.accumulate_record(
sample_state = self._dp_average_query.accumulate_record(
sample_params, sample_state, grads_list)
return sample_state
@ -84,7 +81,7 @@ def make_optimizer_class(cls):
var_list = (
tf.trainable_variables() + tf.get_collection(
tf.GraphKeys.TRAINABLE_RESOURCE_VARIABLES))
sample_state = self._private_query.initial_sample_state(
sample_state = self._dp_average_query.initial_sample_state(
self._global_state, var_list)
if self._unroll_microbatches:
@ -100,15 +97,48 @@ def make_optimizer_class(cls):
_, sample_state = tf.while_loop(cond_fn, body_fn, [idx, sample_state])
final_grads, self._global_state = (
self._private_query.get_noised_average(sample_state,
self._global_state))
self._dp_average_query.get_noised_result(
sample_state, self._global_state))
return list(zip(final_grads, var_list))
return DPOptimizerClass
def make_gaussian_optimizer_class(cls):
"""Constructs a DP optimizer with Gaussian averaging of updates."""
class DPGaussianOptimizerClass(make_optimizer_class(cls)):
"""DP subclass of given class cls using Gaussian averaging."""
def __init__(
self,
l2_norm_clip,
noise_multiplier,
num_microbatches,
unroll_microbatches=False,
*args, # pylint: disable=keyword-arg-before-vararg
**kwargs):
dp_average_query = gaussian_query.GaussianAverageQuery(
l2_norm_clip, l2_norm_clip * noise_multiplier, num_microbatches)
super(DPGaussianOptimizerClass, self).__init__(
dp_average_query,
num_microbatches,
unroll_microbatches,
*args,
**kwargs)
return DPGaussianOptimizerClass
DPAdagradOptimizer = make_optimizer_class(tf.train.AdagradOptimizer)
DPAdamOptimizer = make_optimizer_class(tf.train.AdamOptimizer)
DPGradientDescentOptimizer = make_optimizer_class(
tf.train.GradientDescentOptimizer)
DPAdagradGaussianOptimizer = make_gaussian_optimizer_class(
tf.train.AdagradOptimizer)
DPAdamGaussianOptimizer = make_gaussian_optimizer_class(tf.train.AdamOptimizer)
DPGradientDescentGaussianOptimizer = make_gaussian_optimizer_class(
tf.train.GradientDescentOptimizer)

59
privacy/optimizers/dp_optimizer_test.py
View file

@ -23,6 +23,7 @@ import numpy as np
import tensorflow as tf
from privacy.optimizers import dp_optimizer
from privacy.optimizers import gaussian_query
def loss(val0, val1):
@ -51,9 +52,11 @@ class DPOptimizerTest(tf.test.TestCase, parameterized.TestCase):
var0 = tf.Variable([1.0, 2.0])
data0 = tf.Variable([[3.0, 4.0], [5.0, 6.0], [7.0, 8.0], [-1.0, 0.0]])
dp_average_query = gaussian_query.GaussianAverageQuery(
1.0e9, 0.0, num_microbatches)
opt = cls(
l2_norm_clip=1.0e9,
noise_multiplier=0.0,
dp_average_query,
num_microbatches=num_microbatches,
learning_rate=2.0)
@ -76,11 +79,9 @@ class DPOptimizerTest(tf.test.TestCase, parameterized.TestCase):
var0 = tf.Variable([0.0, 0.0])
data0 = tf.Variable([[3.0, 4.0], [6.0, 8.0]])
opt = cls(
l2_norm_clip=1.0,
noise_multiplier=0.0,
num_microbatches=1,
learning_rate=2.0)
dp_average_query = gaussian_query.GaussianAverageQuery(1.0, 0.0, 1)
opt = cls(dp_average_query, num_microbatches=1, learning_rate=2.0)
self.evaluate(tf.global_variables_initializer())
# Fetch params to validate initial values
@ -100,11 +101,9 @@ class DPOptimizerTest(tf.test.TestCase, parameterized.TestCase):
var0 = tf.Variable([0.0])
data0 = tf.Variable([[0.0]])
opt = cls(
l2_norm_clip=4.0,
noise_multiplier=2.0,
num_microbatches=1,
learning_rate=2.0)
dp_average_query = gaussian_query.GaussianAverageQuery(4.0, 8.0, 1)
opt = cls(dp_average_query, num_microbatches=1, learning_rate=2.0)
self.evaluate(tf.global_variables_initializer())
# Fetch params to validate initial values
@ -143,9 +142,9 @@ class DPOptimizerTest(tf.test.TestCase, parameterized.TestCase):
vector_loss = tf.squared_difference(labels, preds)
scalar_loss = tf.reduce_mean(vector_loss)
dp_average_query = gaussian_query.GaussianAverageQuery(1.0, 0.0, 1)
optimizer = dp_optimizer.DPGradientDescentOptimizer(
l2_norm_clip=1.0,
noise_multiplier=0.0,
dp_average_query,
num_microbatches=1,
learning_rate=1.0)
global_step = tf.train.get_global_step()
@ -183,9 +182,10 @@ class DPOptimizerTest(tf.test.TestCase, parameterized.TestCase):
var0 = tf.Variable([1.0, 2.0])
data0 = tf.Variable([[3.0, 4.0], [5.0, 6.0], [7.0, 8.0], [-1.0, 0.0]])
dp_average_query = (
gaussian_query.GaussianAverageQuery(1.0e9, 0.0, 4))
opt = cls(
l2_norm_clip=1.0e9,
noise_multiplier=0.0,
dp_average_query,
num_microbatches=4,
learning_rate=2.0,
unroll_microbatches=True)
@ -200,6 +200,33 @@ class DPOptimizerTest(tf.test.TestCase, parameterized.TestCase):
grads_and_vars = sess.run(gradient_op)
self.assertAllCloseAccordingToType([-2.5, -2.5], grads_and_vars[0][0])
@parameterized.named_parameters(
('DPGradientDescent', dp_optimizer.DPGradientDescentGaussianOptimizer),
('DPAdagrad', dp_optimizer.DPAdagradGaussianOptimizer),
('DPAdam', dp_optimizer.DPAdamGaussianOptimizer))
def testDPGaussianOptimizerClass(self, cls):
with self.cached_session() as sess:
var0 = tf.Variable([0.0])
data0 = tf.Variable([[0.0]])
opt = cls(
l2_norm_clip=4.0,
noise_multiplier=2.0,
num_microbatches=1,
learning_rate=2.0)
self.evaluate(tf.global_variables_initializer())
# Fetch params to validate initial values
self.assertAllClose([0.0], self.evaluate(var0))
gradient_op = opt.compute_gradients(loss(data0, var0), [var0])
grads = []
for _ in range(1000):
grads_and_vars = sess.run(gradient_op)
grads.append(grads_and_vars[0][0])
# Test standard deviation is close to l2_norm_clip * noise_multiplier.
self.assertNear(np.std(grads), 2.0 * 4.0, 0.5)
if __name__ == '__main__':
tf.test.main()

50
privacy/optimizers/private_queries.py → privacy/optimizers/dp_query.py
View file

@ -22,14 +22,14 @@ from __future__ import print_function
import abc
class PrivateQuery(object):
class DPQuery(object):
"""Interface for differentially private query mechanisms."""
__metaclass__ = abc.ABCMeta
@abc.abstractmethod
def initial_global_state(self):
"""Returns the initial global state for the PrivateQuery."""
"""Returns the initial global state for the DPQuery."""
pass
@abc.abstractmethod
@ -72,7 +72,7 @@ class PrivateQuery(object):
pass
@abc.abstractmethod
def get_query_result(self, sample_state, global_state):
def get_noised_result(self, sample_state, global_state):
"""Gets query result after all records of sample have been accumulated.
Args:
@ -84,47 +84,3 @@ class PrivateQuery(object):
query and "new_global_state" is the updated global state.
"""
pass
class PrivateSumQuery(PrivateQuery):
"""Interface for differentially private mechanisms to compute a sum."""
@abc.abstractmethod
def get_noised_sum(self, sample_state, global_state):
"""Gets estimate of sum after all records of sample have been accumulated.
Args:
sample_state: The sample state after all records have been accumulated.
global_state: The global state.
Returns:
A tuple (estimate, new_global_state) where "estimate" is the estimated
sum of the records and "new_global_state" is the updated global state.
"""
pass
def get_query_result(self, sample_state, global_state):
"""Delegates to get_noised_sum."""
return self.get_noised_sum(sample_state, global_state)
class PrivateAverageQuery(PrivateQuery):
"""Interface for differentially private mechanisms to compute an average."""
@abc.abstractmethod
def get_noised_average(self, sample_state, global_state):
"""Gets average estimate after all records of sample have been accumulated.
Args:
sample_state: The sample state after all records have been accumulated.
global_state: The global state.
Returns:
A tuple (estimate, new_global_state) where "estimate" is the estimated
average of the records and "new_global_state" is the updated global state.
"""
pass
def get_query_result(self, sample_state, global_state):
"""Delegates to get_noised_average."""
return self.get_noised_average(sample_state, global_state)

23
privacy/optimizers/gaussian_query.py
View file

@ -12,7 +12,7 @@
# See the License for the specific language governing permissions and
# limitations under the License.
"""Implements PrivateQuery interface for Gaussian average queries.
"""Implements DPQuery interface for Gaussian average queries.
"""
from __future__ import absolute_import
@ -23,13 +23,13 @@ import collections
import tensorflow as tf
from privacy.optimizers import private_queries
from privacy.optimizers import dp_query
nest = tf.contrib.framework.nest
class GaussianSumQuery(private_queries.PrivateSumQuery):
"""Implements PrivateQuery interface for Gaussian sum queries.
class GaussianSumQuery(dp_query.DPQuery):
"""Implements DPQuery interface for Gaussian sum queries.
Accumulates clipped vectors, then adds Gaussian noise to the sum.
"""
@ -94,7 +94,7 @@ class GaussianSumQuery(private_queries.PrivateSumQuery):
clipped = nest.pack_sequence_as(record, clipped_as_list)
return nest.map_structure(tf.add, sample_state, clipped)
def get_noised_sum(self, sample_state, global_state):
def get_noised_result(self, sample_state, global_state):
"""Gets noised sum after all records of sample have been accumulated.
Args:
@ -111,10 +111,15 @@ class GaussianSumQuery(private_queries.PrivateSumQuery):
return nest.map_structure(add_noise, sample_state), global_state
class GaussianAverageQuery(private_queries.PrivateAverageQuery):
"""Implements PrivateQuery interface for Gaussian average queries.
class GaussianAverageQuery(dp_query.DPQuery):
"""Implements DPQuery interface for Gaussian average queries.
Accumulates clipped vectors, adds Gaussian noise, and normalizes.
Note that we use "fixed-denominator" estimation: the denominator should be
specified as the expected number of records per sample. Accumulating the
denominator separately would also be possible but would be produce a higher
variance estimator.
"""
# pylint: disable=invalid-name
@ -177,7 +182,7 @@ class GaussianAverageQuery(private_queries.PrivateAverageQuery):
"""
return self._numerator.accumulate_record(params, sample_state, record)
def get_noised_average(self, sample_state, global_state):
def get_noised_result(self, sample_state, global_state):
"""Gets noised average after all records of sample have been accumulated.
Args:
@ -188,7 +193,7 @@ class GaussianAverageQuery(private_queries.PrivateAverageQuery):
A tuple (estimate, new_global_state) where "estimate" is the estimated
average of the records and "new_global_state" is the updated global state.
"""
noised_sum, new_sum_global_state = self._numerator.get_noised_sum(
noised_sum, new_sum_global_state = self._numerator.get_noised_result(
sample_state, global_state.sum_state)
new_global_state = self._GlobalState(
new_sum_global_state, global_state.denominator)

32
privacy/optimizers/gaussian_query_test.py
View file

@ -23,25 +23,7 @@ import numpy as np
import tensorflow as tf
from privacy.optimizers import gaussian_query
def _run_query(query, records):
"""Executes query on the given set of records as a single sample.
Args:
query: A PrivateQuery to run.
records: An iterable containing records to pass to the query.
Returns:
The result of the query.
"""
global_state = query.initial_global_state()
params = query.derive_sample_params(global_state)
sample_state = query.initial_sample_state(global_state, next(iter(records)))
for record in records:
sample_state = query.accumulate_record(params, sample_state, record)
result, _ = query.get_query_result(sample_state, global_state)
return result
from privacy.optimizers import test_utils
class GaussianQueryTest(tf.test.TestCase, parameterized.TestCase):
@ -53,7 +35,7 @@ class GaussianQueryTest(tf.test.TestCase, parameterized.TestCase):
query = gaussian_query.GaussianSumQuery(
l2_norm_clip=10.0, stddev=0.0)
query_result = _run_query(query, [record1, record2])
query_result = test_utils.run_query(query, [record1, record2])
result = sess.run(query_result)
expected = [1.0, 1.0]
self.assertAllClose(result, expected)
@ -65,7 +47,7 @@ class GaussianQueryTest(tf.test.TestCase, parameterized.TestCase):
query = gaussian_query.GaussianSumQuery(
l2_norm_clip=5.0, stddev=0.0)
query_result = _run_query(query, [record1, record2])
query_result = test_utils.run_query(query, [record1, record2])
result = sess.run(query_result)
expected = [1.0, 1.0]
self.assertAllClose(result, expected)
@ -77,7 +59,7 @@ class GaussianQueryTest(tf.test.TestCase, parameterized.TestCase):
query = gaussian_query.GaussianSumQuery(
l2_norm_clip=5.0, stddev=stddev)
query_result = _run_query(query, [record1, record2])
query_result = test_utils.run_query(query, [record1, record2])
noised_sums = []
for _ in xrange(1000):
@ -93,7 +75,7 @@ class GaussianQueryTest(tf.test.TestCase, parameterized.TestCase):
query = gaussian_query.GaussianAverageQuery(
l2_norm_clip=3.0, sum_stddev=0.0, denominator=2.0)
query_result = _run_query(query, [record1, record2])
query_result = test_utils.run_query(query, [record1, record2])
result = sess.run(query_result)
expected_average = [1.0, 1.0]
self.assertAllClose(result, expected_average)
@ -106,7 +88,7 @@ class GaussianQueryTest(tf.test.TestCase, parameterized.TestCase):
query = gaussian_query.GaussianAverageQuery(
l2_norm_clip=5.0, sum_stddev=sum_stddev, denominator=denominator)
query_result = _run_query(query, [record1, record2])
query_result = test_utils.run_query(query, [record1, record2])
noised_averages = []
for _ in range(1000):
@ -123,7 +105,7 @@ class GaussianQueryTest(tf.test.TestCase, parameterized.TestCase):
def test_incompatible_records(self, record1, record2, error_type):
query = gaussian_query.GaussianSumQuery(1.0, 0.0)
with self.assertRaises(error_type):
_run_query(query, [record1, record2])
test_utils.run_query(query, [record1, record2])
if __name__ == '__main__':

12
privacy/optimizers/nested_query.py
View file

@ -12,7 +12,7 @@
# See the License for the specific language governing permissions and
# limitations under the License.
"""Implements PrivateQuery interface for queries over nested structures.
"""Implements DPQuery interface for queries over nested structures.
"""
from __future__ import absolute_import
@ -22,13 +22,13 @@ from __future__ import print_function
import tensorflow as tf
from privacy.optimizers import private_queries
from privacy.optimizers import dp_query
nest = tf.contrib.framework.nest
class NestedQuery(private_queries.PrivateQuery):
"""Implements PrivateQuery interface for structured queries.
class NestedQuery(dp_query.DPQuery):
"""Implements DPQuery interface for structured queries.
NestedQuery evaluates arbitrary nested structures of queries. Records must be
nested structures of tensors that are compatible (in type and arity) with the
@ -100,7 +100,7 @@ class NestedQuery(private_queries.PrivateQuery):
return self._map_to_queries(
'accumulate_record', params, sample_state, record)
def get_query_result(self, sample_state, global_state):
def get_noised_result(self, sample_state, global_state):
"""Gets query result after all records of sample have been accumulated.
Args:
@ -114,7 +114,7 @@ class NestedQuery(private_queries.PrivateQuery):
for the subqueries.
"""
estimates_and_new_global_states = self._map_to_queries(
'get_query_result', sample_state, global_state)
'get_noised_result', sample_state, global_state)
flat_estimates, flat_new_global_states = zip(
*nest.flatten_up_to(self._queries, estimates_and_new_global_states))

32
privacy/optimizers/nested_query_test.py
View file

@ -25,31 +25,13 @@ import tensorflow as tf
from privacy.optimizers import gaussian_query
from privacy.optimizers import nested_query
from privacy.optimizers import test_utils
nest = tf.contrib.framework.nest
_basic_query = gaussian_query.GaussianSumQuery(1.0, 0.0)
def _run_query(query, records):
"""Executes query on the given set of records as a single sample.
Args:
query: A PrivateQuery to run.
records: An iterable containing records to pass to the query.
Returns:
The result of the query.
"""
global_state = query.initial_global_state()
params = query.derive_sample_params(global_state)
sample_state = query.initial_sample_state(global_state, next(iter(records)))
for record in records:
sample_state = query.accumulate_record(params, sample_state, record)
result, _ = query.get_query_result(sample_state, global_state)
return result
class NestedQueryTest(tf.test.TestCase, parameterized.TestCase):
def test_nested_gaussian_sum_no_clip_no_noise(self):
@ -64,7 +46,7 @@ class NestedQueryTest(tf.test.TestCase, parameterized.TestCase):
record1 = [1.0, [2.0, 3.0]]
record2 = [4.0, [3.0, 2.0]]
query_result = _run_query(query, [record1, record2])
query_result = test_utils.run_query(query, [record1, record2])
result = sess.run(query_result)
expected = [5.0, [5.0, 5.0]]
self.assertAllClose(result, expected)
@ -81,7 +63,7 @@ class NestedQueryTest(tf.test.TestCase, parameterized.TestCase):
record1 = [1.0, [2.0, 3.0]]
record2 = [4.0, [3.0, 2.0]]
query_result = _run_query(query, [record1, record2])
query_result = test_utils.run_query(query, [record1, record2])
result = sess.run(query_result)
expected = [1.0, [1.0, 1.0]]
self.assertAllClose(result, expected)
@ -98,7 +80,7 @@ class NestedQueryTest(tf.test.TestCase, parameterized.TestCase):
record1 = [1.0, [12.0, 9.0]] # Clipped to [1.0, [4.0, 3.0]]
record2 = [5.0, [1.0, 2.0]] # Clipped to [4.0, [1.0, 2.0]]
query_result = _run_query(query, [record1, record2])
query_result = test_utils.run_query(query, [record1, record2])
result = sess.run(query_result)
expected = [1.0, [1.0, 1.0]]
self.assertAllClose(result, expected)
@ -118,7 +100,7 @@ class NestedQueryTest(tf.test.TestCase, parameterized.TestCase):
record1 = [{'a': 0.0, 'b': 2.71828}, {'c': (-4.0, 6.0), 'd': [-4.0]}]
record2 = [{'a': 3.14159, 'b': 0.0}, {'c': (6.0, -4.0), 'd': [5.0]}]
query_result = _run_query(query, [record1, record2])
query_result = test_utils.run_query(query, [record1, record2])
result = sess.run(query_result)
expected = [{'a': 1.0, 'b': 1.0}, {'c': (1.0, 1.0), 'd': [1.0]}]
self.assertAllClose(result, expected)
@ -137,7 +119,7 @@ class NestedQueryTest(tf.test.TestCase, parameterized.TestCase):
record1 = (3.0, [2.0, 1.5])
record2 = (0.0, [-1.0, -3.5])
query_result = _run_query(query, [record1, record2])
query_result = test_utils.run_query(query, [record1, record2])
noised_averages = []
for _ in range(1000):
@ -157,7 +139,7 @@ class NestedQueryTest(tf.test.TestCase, parameterized.TestCase):
def test_record_incompatible_with_query(
self, queries, record, error_type):
with self.assertRaises(error_type):
_run_query(nested_query.NestedQuery(queries), [record])
test_utils.run_query(nested_query.NestedQuery(queries), [record])
if __name__ == '__main__':

18
privacy/optimizers/no_privacy_query.py
View file

@ -11,7 +11,7 @@
# 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.
"""Implements PrivateQuery interface for no privacy average queries."""
"""Implements DPQuery interface for no privacy average queries."""
from __future__ import absolute_import
from __future__ import division
@ -19,13 +19,13 @@ from __future__ import print_function
import tensorflow as tf
from privacy.optimizers import private_queries
from privacy.optimizers import dp_query
nest = tf.contrib.framework.nest
class NoPrivacySumQuery(private_queries.PrivateSumQuery):
"""Implements PrivateQuery interface for a sum query with no privacy.
class NoPrivacySumQuery(dp_query.DPQuery):
"""Implements DPQuery interface for a sum query with no privacy.
Accumulates vectors without clipping or adding noise.
"""
@ -53,13 +53,13 @@ class NoPrivacySumQuery(private_queries.PrivateSumQuery):
return nest.map_structure(add_weighted, sample_state, record)
def get_noised_sum(self, sample_state, global_state):
def get_noised_result(self, sample_state, global_state):
"""See base class."""
return sample_state, global_state
class NoPrivacyAverageQuery(private_queries.PrivateAverageQuery):
"""Implements PrivateQuery interface for an average query with no privacy.
class NoPrivacyAverageQuery(dp_query.DPQuery):
"""Implements DPQuery interface for an average query with no privacy.
Accumulates vectors and normalizes by the total number of accumulated vectors.
"""
@ -89,10 +89,10 @@ class NoPrivacyAverageQuery(private_queries.PrivateAverageQuery):
params, sum_sample_state, record, weight),
tf.add(denominator, weight))
def get_noised_average(self, sample_state, global_state):
def get_noised_result(self, sample_state, global_state):
"""See base class."""
sum_sample_state, denominator = sample_state
exact_sum, new_global_state = self._numerator.get_noised_sum(
exact_sum, new_global_state = self._numerator.get_noised_result(
sum_sample_state, global_state)
def normalize(v):

38
privacy/optimizers/no_privacy_query_test.py
View file

@ -22,31 +22,7 @@ from absl.testing import parameterized
import tensorflow as tf
from privacy.optimizers import no_privacy_query
def _run_query(query, records, weights=None):
"""Executes query on the given set of records as a single sample.
Args:
query: A PrivateQuery to run.
records: An iterable containing records to pass to the query.
weights: An optional iterable containing the weights of the records.
Returns:
The result of the query.
"""
global_state = query.initial_global_state()
params = query.derive_sample_params(global_state)
sample_state = query.initial_sample_state(global_state, next(iter(records)))
if weights is None:
for record in records:
sample_state = query.accumulate_record(params, sample_state, record)
else:
for weight, record in zip(weights, records):
sample_state = query.accumulate_record(params, sample_state, record,
weight)
result, _ = query.get_query_result(sample_state, global_state)
return result
from privacy.optimizers import test_utils
class NoPrivacyQueryTest(tf.test.TestCase, parameterized.TestCase):
@ -57,7 +33,7 @@ class NoPrivacyQueryTest(tf.test.TestCase, parameterized.TestCase):
record2 = tf.constant([-1.0, 1.0])
query = no_privacy_query.NoPrivacySumQuery()
query_result = _run_query(query, [record1, record2])
query_result = test_utils.run_query(query, [record1, record2])
result = sess.run(query_result)
expected = [1.0, 1.0]
self.assertAllClose(result, expected)
@ -71,7 +47,8 @@ class NoPrivacyQueryTest(tf.test.TestCase, parameterized.TestCase):
weight2 = 2
query = no_privacy_query.NoPrivacySumQuery()
query_result = _run_query(query, [record1, record2], [weight1, weight2])
query_result = test_utils.run_query(
query, [record1, record2], [weight1, weight2])
result = sess.run(query_result)
expected = [0.0, 2.0]
self.assertAllClose(result, expected)
@ -82,7 +59,7 @@ class NoPrivacyQueryTest(tf.test.TestCase, parameterized.TestCase):
record2 = tf.constant([-1.0, 2.0])
query = no_privacy_query.NoPrivacyAverageQuery()
query_result = _run_query(query, [record1, record2])
query_result = test_utils.run_query(query, [record1, record2])
result = sess.run(query_result)
expected = [2.0, 1.0]
self.assertAllClose(result, expected)
@ -96,7 +73,8 @@ class NoPrivacyQueryTest(tf.test.TestCase, parameterized.TestCase):
weight2 = 3
query = no_privacy_query.NoPrivacyAverageQuery()
query_result = _run_query(query, [record1, record2], [weight1, weight2])
query_result = test_utils.run_query(
query, [record1, record2], [weight1, weight2])
result = sess.run(query_result)
expected = [0.25, 0.75]
self.assertAllClose(result, expected)
@ -108,7 +86,7 @@ class NoPrivacyQueryTest(tf.test.TestCase, parameterized.TestCase):
def test_incompatible_records(self, record1, record2, error_type):
query = no_privacy_query.NoPrivacySumQuery()
with self.assertRaises(error_type):
_run_query(query, [record1, record2])
test_utils.run_query(query, [record1, record2])
if __name__ == '__main__':

46
privacy/optimizers/test_utils.py Normal file
View file

@ -0,0 +1,46 @@
# Copyright 2019, The TensorFlow Authors.
#
# 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.
"""Utility methods for testing private queries.
Utility methods for testing private queries.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
def run_query(query, records, weights=None):
"""Executes query on the given set of records as a single sample.
Args:
query: A PrivateQuery to run.
records: An iterable containing records to pass to the query.
weights: An optional iterable containing the weights of the records.
Returns:
The result of the query.
"""
global_state = query.initial_global_state()
params = query.derive_sample_params(global_state)
sample_state = query.initial_sample_state(global_state, next(iter(records)))
if weights is None:
for record in records:
sample_state = query.accumulate_record(params, sample_state, record)
else:
for weight, record in zip(weights, records):
sample_state = query.accumulate_record(
params, sample_state, record, weight)
result, _ = query.get_noised_result(sample_state, global_state)
return result

12
tutorials/mnist_dpsgd_tutorial.py
View file

@ -70,15 +70,13 @@ def cnn_model_fn(features, labels, mode):
if FLAGS.dpsgd:
# Use DP version of GradientDescentOptimizer. For illustration purposes,
# we do that here by calling make_optimizer_class() explicitly, though DP
# we do that here by calling optimizer_from_args() explicitly, though DP
# versions of standard optimizers are available in dp_optimizer.
dp_optimizer_class = dp_optimizer.make_optimizer_class(
tf.train.GradientDescentOptimizer)
optimizer = dp_optimizer_class(
learning_rate=FLAGS.learning_rate,
noise_multiplier=FLAGS.noise_multiplier,
optimizer = dp_optimizer.DPGradientDescentGaussianOptimizer(
l2_norm_clip=FLAGS.l2_norm_clip,
num_microbatches=FLAGS.microbatches)
noise_multiplier=FLAGS.noise_multiplier,
num_microbatches=FLAGS.microbatches,
learning_rate=FLAGS.learning_rate)
opt_loss = vector_loss
else:
optimizer = tf.train.GradientDescentOptimizer(