def test_complex_nested_query(self): with self.cached_session() as sess: query_ab = gaussian_query.GaussianSumQuery(l2_norm_clip=1.0, stddev=0.0) query_c = gaussian_query.GaussianAverageQuery(l2_norm_clip=10.0, sum_stddev=0.0, denominator=2.0) query_d = gaussian_query.GaussianSumQuery(l2_norm_clip=10.0, stddev=0.0) query = nested_query.NestedQuery( [query_ab, { 'c': query_c, 'd': [query_d] }]) record1 = [{ 'a': 0.0, 'b': 2.71828 }, { 'c': (-4.0, 6.0), 'd': [-4.0] }] record2 = [{ 'a': 3.14159, 'b': 0.0 }, { 'c': (6.0, -4.0), 'd': [5.0] }] query_result = _run_query(query, [record1, record2]) result = sess.run(query_result) expected = [{'a': 1.0, 'b': 1.0}, {'c': (1.0, 1.0), 'd': [1.0]}] self.assertAllClose(result, expected)
def test_nested_gaussian_sum_no_clip_no_noise(self): with self.cached_session() as sess: query1 = gaussian_query.GaussianSumQuery(l2_norm_clip=10.0, stddev=0.0) query2 = gaussian_query.GaussianSumQuery(l2_norm_clip=10.0, stddev=0.0) query = nested_query.NestedQuery([query1, query2]) record1 = [1.0, [2.0, 3.0]] record2 = [4.0, [3.0, 2.0]] query_result = _run_query(query, [record1, record2]) result = sess.run(query_result) expected = [5.0, [5.0, 5.0]] self.assertAllClose(result, expected)
def test_nested_query_with_noise(self): with self.cached_session() as sess: sum_stddev = 2.71828 denominator = 3.14159 query1 = gaussian_query.GaussianSumQuery(l2_norm_clip=1.5, stddev=sum_stddev) query2 = gaussian_query.GaussianAverageQuery( l2_norm_clip=0.5, sum_stddev=sum_stddev, denominator=denominator) query = nested_query.NestedQuery((query1, query2)) record1 = (3.0, [2.0, 1.5]) record2 = (0.0, [-1.0, -3.5]) query_result = _run_query(query, [record1, record2]) noised_averages = [] for _ in xrange(1000): noised_averages.append(nest.flatten(sess.run(query_result))) result_stddev = np.std(noised_averages, 0) avg_stddev = sum_stddev / denominator expected_stddev = [sum_stddev, avg_stddev, avg_stddev] self.assertArrayNear(result_stddev, expected_stddev, 0.1)
def test_gaussian_sum_with_clip_no_noise(self): with self.cached_session() as sess: record1 = tf.constant([-6.0, 8.0]) # Clipped to [-3.0, 4.0]. record2 = tf.constant([4.0, -3.0]) # Not clipped. query = gaussian_query.GaussianSumQuery( l2_norm_clip=5.0, stddev=0.0) query_result, _ = test_utils.run_query(query, [record1, record2]) result = sess.run(query_result) expected = [1.0, 1.0] self.assertAllClose(result, expected)
def test_gaussian_sum_no_clip_no_noise(self): with self.cached_session() as sess: record1 = tf.constant([2.0, 0.0]) record2 = tf.constant([-1.0, 1.0]) query = gaussian_query.GaussianSumQuery( l2_norm_clip=10.0, stddev=0.0) query_result, _ = test_utils.run_query(query, [record1, record2]) result = sess.run(query_result) expected = [1.0, 1.0] self.assertAllClose(result, expected)
def test_gaussian_sum_with_noise(self): with self.cached_session() as sess: record1, record2 = 2.71828, 3.14159 stddev = 1.0 query = gaussian_query.GaussianSumQuery( l2_norm_clip=5.0, stddev=stddev) query_result, _ = test_utils.run_query(query, [record1, record2]) noised_sums = [] for _ in xrange(1000): noised_sums.append(sess.run(query_result)) result_stddev = np.std(noised_sums) self.assertNear(result_stddev, stddev, 0.1)
def test_gaussian_sum_with_changing_clip_no_noise(self): with self.cached_session() as sess: record1 = tf.constant([-6.0, 8.0]) # Clipped to [-3.0, 4.0]. record2 = tf.constant([4.0, -3.0]) # Not clipped. l2_norm_clip = tf.Variable(5.0) l2_norm_clip_placeholder = tf.placeholder(tf.float32) assign_l2_norm_clip = tf.assign(l2_norm_clip, l2_norm_clip_placeholder) query = gaussian_query.GaussianSumQuery( l2_norm_clip=l2_norm_clip, stddev=0.0) query_result, _ = test_utils.run_query(query, [record1, record2]) self.evaluate(tf.global_variables_initializer()) result = sess.run(query_result) expected = [1.0, 1.0] self.assertAllClose(result, expected) sess.run(assign_l2_norm_clip, {l2_norm_clip_placeholder: 0.0}) result = sess.run(query_result) expected = [0.0, 0.0] self.assertAllClose(result, expected)
def test_sum_query(self): record1 = tf.constant([2.0, 0.0]) record2 = tf.constant([-1.0, 1.0]) population_size = tf.Variable(0) selection_probability = tf.Variable(0.0) ledger = privacy_ledger.PrivacyLedger(population_size, selection_probability, 50, 50) query = gaussian_query.GaussianSumQuery(l2_norm_clip=10.0, stddev=0.0, ledger=ledger) query = privacy_ledger.QueryWithLedger(query, ledger) # First sample. tf.assign(population_size, 10) tf.assign(selection_probability, 0.1) test_utils.run_query(query, [record1, record2]) expected_queries = [[10.0, 0.0]] formatted = ledger.get_formatted_ledger_eager() sample_1 = formatted[0] self.assertAllClose(sample_1.population_size, 10.0) self.assertAllClose(sample_1.selection_probability, 0.1) self.assertAllClose(sample_1.queries, expected_queries) # Second sample. tf.assign(population_size, 20) tf.assign(selection_probability, 0.2) test_utils.run_query(query, [record1, record2]) formatted = ledger.get_formatted_ledger_eager() sample_1, sample_2 = formatted self.assertAllClose(sample_1.population_size, 10.0) self.assertAllClose(sample_1.selection_probability, 0.1) self.assertAllClose(sample_1.queries, expected_queries) self.assertAllClose(sample_2.population_size, 20.0) self.assertAllClose(sample_2.selection_probability, 0.2) self.assertAllClose(sample_2.queries, expected_queries)
def test_incompatible_records(self, record1, record2, error_type): query = gaussian_query.GaussianSumQuery(1.0, 0.0) with self.assertRaises(error_type): test_utils.run_query(query, [record1, record2])
"""Tests for NestedQuery.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function from absl.testing import parameterized import numpy as np import tensorflow as tf from privacy.optimizers import gaussian_query from privacy.optimizers import nested_query nest = tf.contrib.framework.nest _basic_query = gaussian_query.GaussianSumQuery(1.0, 0.0) try: xrange except NameError: xrange = range def _run_query(query, records): """Executes query on the given set of records as a single sample. Args: query: A PrivateQuery to run. records: An iterable containing records to pass to the query. Returns: