1d1a6e087a
1. Split DPQuery.accumulate_record function into preprocess_record and accumulate_preprocessed_record. 2. Add merge_sample_state function. 3. Add default implementations for some functions in DPQuery, and add base class SumAggregationDPQuery that implements some more. Only get_noised_result is still abstract. 4. Enforce that all states and parameters used as inputs and outputs to DPQuery functions are nested structures of tensors by replacing numbers with constants and Nones with empty tuples. PiperOrigin-RevId: 247975791
130 lines
4.4 KiB
Python
130 lines
4.4 KiB
Python
# Copyright 2018, The TensorFlow Authors.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Implements DPQuery interface for Gaussian average queries.
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"""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from distutils.version import LooseVersion
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import tensorflow as tf
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from privacy.dp_query import dp_query
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from privacy.dp_query import normalized_query
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if LooseVersion(tf.__version__) < LooseVersion('2.0.0'):
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nest = tf.contrib.framework.nest
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else:
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nest = tf.nest
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class GaussianSumQuery(dp_query.SumAggregationDPQuery):
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"""Implements DPQuery interface for Gaussian sum queries.
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Accumulates clipped vectors, then adds Gaussian noise to the sum.
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"""
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def __init__(self, l2_norm_clip, stddev, ledger=None):
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"""Initializes the GaussianSumQuery.
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Args:
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l2_norm_clip: The clipping norm to apply to the global norm of each
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record.
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stddev: The stddev of the noise added to the sum.
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ledger: The privacy ledger to which queries should be recorded.
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"""
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self._l2_norm_clip = tf.cast(l2_norm_clip, tf.float32)
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self._stddev = tf.cast(stddev, tf.float32)
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self._ledger = ledger
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def derive_sample_params(self, global_state):
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return self._l2_norm_clip
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def initial_sample_state(self, global_state, template):
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if self._ledger:
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dependencies = [
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self._ledger.record_sum_query(self._l2_norm_clip, self._stddev)
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]
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else:
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dependencies = []
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with tf.control_dependencies(dependencies):
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return nest.map_structure(
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dp_query.zeros_like, template)
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def preprocess_record_impl(self, params, record):
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"""Clips the l2 norm, returning the clipped record and the l2 norm.
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Args:
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params: The parameters for the sample.
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record: The record to be processed.
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Returns:
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A tuple (preprocessed_records, l2_norm) where `preprocessed_records` is
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the structure of preprocessed tensors, and l2_norm is the total l2 norm
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before clipping.
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"""
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l2_norm_clip = params
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record_as_list = nest.flatten(record)
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clipped_as_list, norm = tf.clip_by_global_norm(record_as_list, l2_norm_clip)
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return nest.pack_sequence_as(record, clipped_as_list), norm
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def preprocess_record(self, params, record):
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preprocessed_record, _ = self.preprocess_record_impl(params, record)
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return preprocessed_record
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def get_noised_result(self, sample_state, global_state):
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"""See base class."""
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if LooseVersion(tf.__version__) < LooseVersion('2.0.0'):
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def add_noise(v):
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return v + tf.random_normal(tf.shape(v), stddev=self._stddev)
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else:
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random_normal = tf.random_normal_initializer(stddev=self._stddev)
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def add_noise(v):
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return v + random_normal(tf.shape(v))
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return nest.map_structure(add_noise, sample_state), global_state
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class GaussianAverageQuery(normalized_query.NormalizedQuery):
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"""Implements DPQuery interface for Gaussian average queries.
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Accumulates clipped vectors, adds Gaussian noise, and normalizes.
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Note that we use "fixed-denominator" estimation: the denominator should be
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specified as the expected number of records per sample. Accumulating the
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denominator separately would also be possible but would be produce a higher
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variance estimator.
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"""
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def __init__(self,
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l2_norm_clip,
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sum_stddev,
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denominator,
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ledger=None):
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"""Initializes the GaussianAverageQuery.
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Args:
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l2_norm_clip: The clipping norm to apply to the global norm of each
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record.
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sum_stddev: The stddev of the noise added to the sum (before
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normalization).
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denominator: The normalization constant (applied after noise is added to
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the sum).
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ledger: The privacy ledger to which queries should be recorded.
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"""
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super(GaussianAverageQuery, self).__init__(
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numerator_query=GaussianSumQuery(l2_norm_clip, sum_stddev, ledger),
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denominator=denominator)
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