forked from 626_privacy/tensorflow_privacy
101 lines
3.4 KiB
Python
101 lines
3.4 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 PrivateQuery 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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import tensorflow as tf
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from privacy.optimizers import private_queries
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nest = tf.contrib.framework.nest
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class NoPrivacySumQuery(private_queries.PrivateSumQuery):
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"""Implements PrivateQuery 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 initial_global_state(self):
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"""Returns the initial global state for the NoPrivacySumQuery."""
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return None
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def derive_sample_params(self, global_state):
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"""See base class."""
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del global_state # unused.
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return None
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def initial_sample_state(self, global_state, tensors):
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"""See base class."""
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del global_state # unused.
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return nest.map_structure(tf.zeros_like, tensors)
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def accumulate_record(self, params, sample_state, record, weight=1):
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"""See base class. Optional argument for weighted sum queries."""
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del params # unused.
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def add_weighted(state_tensor, record_tensor):
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return tf.add(state_tensor, weight * record_tensor)
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return nest.map_structure(add_weighted, sample_state, record)
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def get_noised_sum(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(private_queries.PrivateAverageQuery):
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"""Implements PrivateQuery 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 __init__(self):
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"""Initializes the NoPrivacyAverageQuery."""
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self._numerator = NoPrivacySumQuery()
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def initial_global_state(self):
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"""Returns the initial global state for the NoPrivacyAverageQuery."""
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return self._numerator.initial_global_state()
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def derive_sample_params(self, global_state):
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"""See base class."""
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del global_state # unused.
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return None
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def initial_sample_state(self, global_state, tensors):
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"""See base class."""
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return self._numerator.initial_sample_state(global_state, tensors), 0.0
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def accumulate_record(self, params, sample_state, record, weight=1):
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"""See base class. Optional argument for weighted average queries."""
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sum_sample_state, denominator = sample_state
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return (
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self._numerator.accumulate_record(
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params, sum_sample_state, record, weight),
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tf.add(denominator, weight))
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def get_noised_average(self, sample_state, global_state):
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"""See base class."""
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sum_sample_state, denominator = sample_state
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exact_sum, new_global_state = self._numerator.get_noised_sum(
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sum_sample_state, global_state)
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def normalize(v):
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return tf.truediv(v, denominator)
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return nest.map_structure(normalize, exact_sum), new_global_state
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