forked from 626_privacy/tensorflow_privacy
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
137 lines
4.7 KiB
Python
137 lines
4.7 KiB
Python
# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
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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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"""Tests for PrivacyLedger."""
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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.analysis import privacy_ledger
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from privacy.dp_query import gaussian_query
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from privacy.dp_query import nested_query
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from privacy.dp_query import test_utils
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tf.enable_eager_execution()
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class PrivacyLedgerTest(tf.test.TestCase):
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def test_fail_on_probability_zero(self):
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with self.assertRaisesRegexp(ValueError,
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'Selection probability cannot be 0.'):
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privacy_ledger.PrivacyLedger(10, 0)
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def test_basic(self):
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ledger = privacy_ledger.PrivacyLedger(10, 0.1)
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ledger.record_sum_query(5.0, 1.0)
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ledger.record_sum_query(2.0, 0.5)
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ledger.finalize_sample()
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expected_queries = [[5.0, 1.0], [2.0, 0.5]]
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formatted = ledger.get_formatted_ledger_eager()
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sample = formatted[0]
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self.assertAllClose(sample.population_size, 10.0)
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self.assertAllClose(sample.selection_probability, 0.1)
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self.assertAllClose(sorted(sample.queries), sorted(expected_queries))
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def test_sum_query(self):
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record1 = tf.constant([2.0, 0.0])
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record2 = tf.constant([-1.0, 1.0])
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population_size = tf.Variable(0)
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selection_probability = tf.Variable(1.0)
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query = gaussian_query.GaussianSumQuery(
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l2_norm_clip=10.0, stddev=0.0)
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query = privacy_ledger.QueryWithLedger(
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query, population_size, selection_probability)
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# First sample.
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tf.assign(population_size, 10)
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tf.assign(selection_probability, 0.1)
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test_utils.run_query(query, [record1, record2])
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expected_queries = [[10.0, 0.0]]
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formatted = query.ledger.get_formatted_ledger_eager()
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sample_1 = formatted[0]
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self.assertAllClose(sample_1.population_size, 10.0)
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self.assertAllClose(sample_1.selection_probability, 0.1)
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self.assertAllClose(sample_1.queries, expected_queries)
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# Second sample.
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tf.assign(population_size, 20)
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tf.assign(selection_probability, 0.2)
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test_utils.run_query(query, [record1, record2])
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formatted = query.ledger.get_formatted_ledger_eager()
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sample_1, sample_2 = formatted
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self.assertAllClose(sample_1.population_size, 10.0)
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self.assertAllClose(sample_1.selection_probability, 0.1)
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self.assertAllClose(sample_1.queries, expected_queries)
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self.assertAllClose(sample_2.population_size, 20.0)
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self.assertAllClose(sample_2.selection_probability, 0.2)
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self.assertAllClose(sample_2.queries, expected_queries)
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def test_nested_query(self):
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population_size = tf.Variable(0)
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selection_probability = tf.Variable(1.0)
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query1 = gaussian_query.GaussianAverageQuery(
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l2_norm_clip=4.0, sum_stddev=2.0, denominator=5.0)
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query2 = gaussian_query.GaussianAverageQuery(
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l2_norm_clip=5.0, sum_stddev=1.0, denominator=5.0)
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query = nested_query.NestedQuery([query1, query2])
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query = privacy_ledger.QueryWithLedger(
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query, population_size, selection_probability)
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record1 = [1.0, [12.0, 9.0]]
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record2 = [5.0, [1.0, 2.0]]
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# First sample.
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tf.assign(population_size, 10)
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tf.assign(selection_probability, 0.1)
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test_utils.run_query(query, [record1, record2])
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expected_queries = [[4.0, 2.0], [5.0, 1.0]]
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formatted = query.ledger.get_formatted_ledger_eager()
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sample_1 = formatted[0]
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self.assertAllClose(sample_1.population_size, 10.0)
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self.assertAllClose(sample_1.selection_probability, 0.1)
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self.assertAllClose(sorted(sample_1.queries), sorted(expected_queries))
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# Second sample.
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tf.assign(population_size, 20)
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tf.assign(selection_probability, 0.2)
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test_utils.run_query(query, [record1, record2])
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formatted = query.ledger.get_formatted_ledger_eager()
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sample_1, sample_2 = formatted
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self.assertAllClose(sample_1.population_size, 10.0)
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self.assertAllClose(sample_1.selection_probability, 0.1)
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self.assertAllClose(sorted(sample_1.queries), sorted(expected_queries))
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self.assertAllClose(sample_2.population_size, 20.0)
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self.assertAllClose(sample_2.selection_probability, 0.2)
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self.assertAllClose(sorted(sample_2.queries), sorted(expected_queries))
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if __name__ == '__main__':
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tf.test.main()
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