forked from 626_privacy/tensorflow_privacy
4d0ab48c35
The privacy ledger keeps a record of all sampling and query events for analysis post hoc by the privacy accountant. PiperOrigin-RevId: 233094012
134 lines
4.8 KiB
Python
134 lines
4.8 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 tensorflow_privacy.privacy.analysis import privacy_ledger
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from tensorflow_privacy.privacy.optimizers import gaussian_query
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from tensorflow_privacy.privacy.optimizers import nested_query
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from tensorflow_privacy.privacy.optimizers 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_basic(self):
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ledger = privacy_ledger.PrivacyLedger(10, 0.1, 50, 50)
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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(0.0)
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ledger = privacy_ledger.PrivacyLedger(
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population_size, selection_probability, 50, 50)
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query = gaussian_query.GaussianSumQuery(
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l2_norm_clip=10.0, stddev=0.0, ledger=ledger)
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query = privacy_ledger.QueryWithLedger(query, ledger)
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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 = 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 = 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(0.0)
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ledger = privacy_ledger.PrivacyLedger(
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population_size, selection_probability, 50, 50)
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query1 = gaussian_query.GaussianAverageQuery(
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l2_norm_clip=4.0, sum_stddev=2.0, denominator=5.0, ledger=ledger)
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query2 = gaussian_query.GaussianAverageQuery(
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l2_norm_clip=5.0, sum_stddev=1.0, denominator=5.0, ledger=ledger)
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query = nested_query.NestedQuery([query1, query2])
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query = privacy_ledger.QueryWithLedger(query, ledger)
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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 = 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 = 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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