d5dcfec745
set_denominator was added so that the batch size doesn't need to be specified before constructing the optimizer, but it breaks the DPQuery abstraction. Now the optimizer uses a GaussianSumQuery instead of GaussianAverageQuery, and normalization by batch size is done inside the optimizer. Also instead of creating all DPQueries with a PrivacyLedger and then wrapping with QueryWithLedger, it is now sufficient to create the queries with no ledger and QueryWithLedger will construct the ledger and pass it to all inner queries. PiperOrigin-RevId: 251462353
145 lines
4.8 KiB
Python
145 lines
4.8 KiB
Python
# Copyright 2018, 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.
|
|
|
|
"""Implements DPQuery interface for Gaussian average queries.
|
|
"""
|
|
|
|
from __future__ import absolute_import
|
|
from __future__ import division
|
|
from __future__ import print_function
|
|
|
|
import collections
|
|
|
|
from distutils.version import LooseVersion
|
|
import tensorflow as tf
|
|
|
|
from privacy.dp_query import dp_query
|
|
from privacy.dp_query import normalized_query
|
|
|
|
if LooseVersion(tf.__version__) < LooseVersion('2.0.0'):
|
|
nest = tf.contrib.framework.nest
|
|
else:
|
|
nest = tf.nest
|
|
|
|
|
|
class GaussianSumQuery(dp_query.SumAggregationDPQuery):
|
|
"""Implements DPQuery interface for Gaussian sum queries.
|
|
|
|
Accumulates clipped vectors, then adds Gaussian noise to the sum.
|
|
"""
|
|
|
|
# pylint: disable=invalid-name
|
|
_GlobalState = collections.namedtuple(
|
|
'_GlobalState', ['l2_norm_clip', 'stddev'])
|
|
|
|
def __init__(self, l2_norm_clip, stddev):
|
|
"""Initializes the GaussianSumQuery.
|
|
|
|
Args:
|
|
l2_norm_clip: The clipping norm to apply to the global norm of each
|
|
record.
|
|
stddev: The stddev of the noise added to the sum.
|
|
"""
|
|
self._l2_norm_clip = l2_norm_clip
|
|
self._stddev = stddev
|
|
self._ledger = None
|
|
|
|
def set_ledger(self, ledger):
|
|
self._ledger = ledger
|
|
|
|
def make_global_state(self, l2_norm_clip, stddev):
|
|
"""Creates a global state from the given parameters."""
|
|
return self._GlobalState(tf.cast(l2_norm_clip, tf.float32),
|
|
tf.cast(stddev, tf.float32))
|
|
|
|
def initial_global_state(self):
|
|
return self.make_global_state(self._l2_norm_clip, self._stddev)
|
|
|
|
def derive_sample_params(self, global_state):
|
|
return global_state.l2_norm_clip
|
|
|
|
def initial_sample_state(self, global_state, template):
|
|
return nest.map_structure(
|
|
dp_query.zeros_like, template)
|
|
|
|
def preprocess_record_impl(self, params, record):
|
|
"""Clips the l2 norm, returning the clipped record and the l2 norm.
|
|
|
|
Args:
|
|
params: The parameters for the sample.
|
|
record: The record to be processed.
|
|
|
|
Returns:
|
|
A tuple (preprocessed_records, l2_norm) where `preprocessed_records` is
|
|
the structure of preprocessed tensors, and l2_norm is the total l2 norm
|
|
before clipping.
|
|
"""
|
|
l2_norm_clip = params
|
|
record_as_list = nest.flatten(record)
|
|
clipped_as_list, norm = tf.clip_by_global_norm(record_as_list, l2_norm_clip)
|
|
return nest.pack_sequence_as(record, clipped_as_list), norm
|
|
|
|
def preprocess_record(self, params, record):
|
|
preprocessed_record, _ = self.preprocess_record_impl(params, record)
|
|
return preprocessed_record
|
|
|
|
def get_noised_result(self, sample_state, global_state):
|
|
"""See base class."""
|
|
if LooseVersion(tf.__version__) < LooseVersion('2.0.0'):
|
|
def add_noise(v):
|
|
return v + tf.random_normal(tf.shape(v), stddev=global_state.stddev)
|
|
else:
|
|
random_normal = tf.random_normal_initializer(stddev=global_state.stddev)
|
|
def add_noise(v):
|
|
return v + random_normal(tf.shape(v))
|
|
|
|
if self._ledger:
|
|
dependencies = [
|
|
self._ledger.record_sum_query(
|
|
global_state.l2_norm_clip, global_state.stddev)
|
|
]
|
|
else:
|
|
dependencies = []
|
|
with tf.control_dependencies(dependencies):
|
|
return nest.map_structure(add_noise, sample_state), global_state
|
|
|
|
|
|
class GaussianAverageQuery(normalized_query.NormalizedQuery):
|
|
"""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.
|
|
"""
|
|
|
|
def __init__(self,
|
|
l2_norm_clip,
|
|
sum_stddev,
|
|
denominator):
|
|
"""Initializes the GaussianAverageQuery.
|
|
|
|
Args:
|
|
l2_norm_clip: The clipping norm to apply to the global norm of each
|
|
record.
|
|
sum_stddev: The stddev of the noise added to the sum (before
|
|
normalization).
|
|
denominator: The normalization constant (applied after noise is added to
|
|
the sum).
|
|
"""
|
|
super(GaussianAverageQuery, self).__init__(
|
|
numerator_query=GaussianSumQuery(l2_norm_clip, sum_stddev),
|
|
denominator=denominator)
|