forked from 626_privacy/tensorflow_privacy
71 lines
2.5 KiB
Python
71 lines
2.5 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 no privacy average queries."""
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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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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 NoPrivacySumQuery(dp_query.SumAggregationDPQuery):
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"""Implements DPQuery interface for a sum query with no privacy.
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Accumulates vectors without clipping or adding noise.
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"""
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def get_noised_result(self, sample_state, global_state):
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"""See base class."""
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return sample_state, global_state
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class NoPrivacyAverageQuery(dp_query.SumAggregationDPQuery):
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"""Implements DPQuery interface for an average query with no privacy.
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Accumulates vectors and normalizes by the total number of accumulated vectors.
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"""
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def initial_sample_state(self, global_state, template):
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"""See base class."""
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return (
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super(NoPrivacyAverageQuery, self).initial_sample_state(
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global_state, template),
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tf.constant(0.0))
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def preprocess_record(self, params, record, weight=1):
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"""Multiplies record by weight."""
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weighted_record = nest.map_structure(lambda t: weight * t, record)
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return (weighted_record, tf.cast(weight, tf.float32))
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def accumulate_record(self, params, sample_state, record, weight=1):
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"""Accumulates record, multiplying by weight."""
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weighted_record = nest.map_structure(lambda t: weight * t, record)
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return self.accumulate_preprocessed_record(
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sample_state, (weighted_record, tf.cast(weight, tf.float32)))
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def get_noised_result(self, sample_state, global_state):
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"""See base class."""
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sum_state, denominator = sample_state
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return nest.map_structure(
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lambda t: tf.truediv(t, denominator), sum_state), global_state
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