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
116 lines
4.3 KiB
Python
116 lines
4.3 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 queries over nested structures.
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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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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 NestedQuery(dp_query.DPQuery):
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"""Implements DPQuery interface for structured queries.
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NestedQuery evaluates arbitrary nested structures of queries. Records must be
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nested structures of tensors that are compatible (in type and arity) with the
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query structure, but are allowed to have deeper structure within each leaf of
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the query structure. For example, the nested query [q1, q2] is compatible with
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the record [t1, t2] or [t1, (t2, t3)], but not with (t1, t2), [t1] or
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[t1, t2, t3]. The entire substructure of each record corresponding to a leaf
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node of the query structure is routed to the corresponding query. If the same
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tensor should be consumed by multiple sub-queries, it can be replicated in the
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record, for example [t1, t1].
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NestedQuery is intended to allow privacy mechanisms for groups as described in
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[McMahan & Andrew, 2018: "A General Approach to Adding Differential Privacy to
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Iterative Training Procedures" (https://arxiv.org/abs/1812.06210)].
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"""
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def __init__(self, queries):
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"""Initializes the NestedQuery.
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Args:
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queries: A nested structure of queries.
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"""
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self._queries = queries
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def _map_to_queries(self, fn, *inputs, **kwargs):
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def caller(query, *args):
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return getattr(query, fn)(*args, **kwargs)
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return nest.map_structure_up_to(
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self._queries, caller, self._queries, *inputs)
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def set_ledger(self, ledger):
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self._map_to_queries('set_ledger', ledger=ledger)
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def initial_global_state(self):
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"""See base class."""
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return self._map_to_queries('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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return self._map_to_queries('derive_sample_params', global_state)
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def initial_sample_state(self, global_state, template):
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"""See base class."""
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return self._map_to_queries('initial_sample_state', global_state, template)
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def preprocess_record(self, params, record):
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"""See base class."""
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return self._map_to_queries('preprocess_record', params, record)
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def accumulate_preprocessed_record(
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self, sample_state, preprocessed_record):
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"""See base class."""
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return self._map_to_queries(
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'accumulate_preprocessed_record',
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sample_state,
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preprocessed_record)
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def merge_sample_states(self, sample_state_1, sample_state_2):
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return self._map_to_queries(
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'merge_sample_states', sample_state_1, sample_state_2)
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def get_noised_result(self, sample_state, global_state):
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"""Gets query result after all records of sample have been accumulated.
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Args:
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sample_state: The sample state after all records have been accumulated.
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global_state: The global state.
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Returns:
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A tuple (result, new_global_state) where "result" is a structure matching
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the query structure containing the results of the subqueries and
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"new_global_state" is a structure containing the updated global states
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for the subqueries.
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"""
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estimates_and_new_global_states = self._map_to_queries(
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'get_noised_result', sample_state, global_state)
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flat_estimates, flat_new_global_states = zip(
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*nest.flatten_up_to(self._queries, estimates_and_new_global_states))
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return (
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nest.pack_sequence_as(self._queries, flat_estimates),
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nest.pack_sequence_as(self._queries, flat_new_global_states))
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