c8cb3c6b70
1. Rename PrivateQuery to DPQuery. 2. Move construction of DPQuery to outside of optimizer. 3. Remove PrivateAverageQuery and PrivateSumQuery, and rename DPQuery's 'get_query_result' method to 'get_noised_result'. Rename private_queries.py to dp_query.py. 4. Remove thrice-replicated run_query function from the test classes and replace with a single function in new test_utils.py. 5. Add functions gaussian_sum_query_from_noise_multplier and gaussian_average_query_from_noise_multplier. PiperOrigin-RevId: 230595991
123 lines
4.4 KiB
Python
123 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 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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import tensorflow as tf
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from privacy.optimizers import dp_query
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nest = tf.contrib.framework.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):
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def caller(query, *args):
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return getattr(query, fn)(*args)
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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 initial_global_state(self):
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"""Returns the initial global state for the NestedQuery."""
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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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"""Given the global state, derives parameters to use for the next sample.
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Args:
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global_state: The current global state.
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Returns:
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Parameters to use to process records in the next sample.
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"""
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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, tensors):
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"""Returns an initial state to use for the next sample.
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Args:
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global_state: The current global state.
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tensors: A structure of tensors used as a template to create the initial
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sample state.
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Returns: An initial sample state.
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"""
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return self._map_to_queries('initial_sample_state', global_state, tensors)
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def accumulate_record(self, params, sample_state, record):
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"""Accumulates a single record into the sample state.
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Args:
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params: The parameters for the sample.
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sample_state: The current sample state.
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record: The record to accumulate.
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Returns:
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The updated sample state.
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"""
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return self._map_to_queries(
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'accumulate_record', params, sample_state, record)
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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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