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statistical_tests_utils_test.cc
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statistical_tests_utils_test.cc
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//
// Copyright 2021 Google LLC
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
//
#include "testing/statistical_tests_utils.h"
#include "google/protobuf/text_format.h"
#include "base/testing/proto_matchers.h"
#include "gmock/gmock.h"
#include "gtest/gtest.h"
#include "algorithms/util.h"
#include "proto/testing/statistical_tests.pb.h"
namespace differential_privacy::testing {
namespace {
const double kLowL2Tolerance = 0.000000001;
const double kDefaultL2Tolerance = 0.001;
const double kHighL2Tolerance = 0.5;
const double kDefaultEpsilon = 1.0;
const double kDefaultDelta = 0.00001;
const double kLowDeltaTolerance = 0.0000000001;
const double kDefaultDeltaTolerance = 0.00001;
const double kHighDeltaTolerance = 0.5;
const double kNumSamples = 1000000;
// A callable object that will return the items in its input vector in order.
struct VectorGenerator {
std::vector<double> samples_;
VectorGenerator(std::vector<double> samples) : samples_(samples) {}
double operator()() {
double to_return = *samples_.begin();
samples_.erase(samples_.begin());
return to_return;
}
};
TEST(ClosenessVoteTest, AcceptsIdenticalSamples) {
std::vector<double> samples = {1.0, 2.0, 2.0, 3.0, 3.0, 3.0, 4.0, 4.0,
4.0, 4.0, 5.0, 5.0, 5.0, 5.0, 5.0};
// We use granularity = 1.0 because the samples are already multiples of 1.0.
EXPECT_TRUE(GenerateClosenessVote(VectorGenerator(samples),
VectorGenerator(samples), samples.size(),
kLowL2Tolerance, /*granularity=*/1.0));
EXPECT_TRUE(GenerateClosenessVote(VectorGenerator(samples),
VectorGenerator(samples), samples.size(),
kHighL2Tolerance, /*granularity=*/1.0));
}
TEST(ClosenessVoteTest, RejectsDifferentSamples) {
std::vector<double> samples_a = {1.0, 2.0, 2.0, 3.0, 3.0, 3.0, 4.0, 4.0,
4.0, 4.0, 5.0, 5.0, 5.0, 5.0, 5.0};
std::vector<double> samples_b = {1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 2.0, 2.0,
2.0, 3.0, 3.0, 3.0, 4.0, 4.0, 5.0};
// We use granularity = 1.0 because the samples are already multiples of 1.0.
EXPECT_FALSE(GenerateClosenessVote(
VectorGenerator(samples_a), VectorGenerator(samples_b), samples_a.size(),
kDefaultL2Tolerance, /*granularity=*/1.0));
EXPECT_FALSE(GenerateClosenessVote(
VectorGenerator(samples_a), VectorGenerator(samples_b), samples_a.size(),
kLowL2Tolerance, /*granularity=*/1.0));
}
TEST(ClosenessVoteTest, AcceptsDifferentSamplesWithHighTolernace) {
std::vector<double> samples_a = {1.0, 2.0, 2.0, 3.0, 3.0, 3.0, 4.0, 4.0,
4.0, 4.0, 5.0, 5.0, 5.0, 5.0, 5.0};
std::vector<double> samples_b = {1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 2.0, 2.0,
2.0, 3.0, 3.0, 3.0, 4.0, 4.0, 5.0};
// We use granularity = 1.0 because the samples are already multiples of 1.0.
EXPECT_TRUE(GenerateClosenessVote(
VectorGenerator(samples_a), VectorGenerator(samples_b), samples_a.size(),
kHighL2Tolerance, /*granularity=*/1.0));
}
TEST(ClosenessVoteTest, InvariantToSampleOrder) {
std::vector<double> samples_a = {1.0, 2.0, 2.0, 3.0, 3.0, 3.0, 4.0, 4.0,
4.0, 4.0, 5.0, 5.0, 5.0, 5.0, 5.0};
std::vector<double> samples_a_unsorted = {5.0, 3.0, 2.0, 3.0, 1.0,
2.0, 5.0, 3.0, 4.0, 5.0,
5.0, 4.0, 5.0, 4.0, 4.0};
std::vector<double> samples_b = {1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 2.0, 2.0,
2.0, 3.0, 3.0, 3.0, 4.0, 4.0, 5.0};
std::vector<double> samples_b_unsorted = {4.0, 1.0, 5.0, 1.0, 4.0,
2.0, 3.0, 3.0, 1.0, 2.0,
2.0, 1.0, 3.0, 1.0, 2.0};
EXPECT_TRUE(GenerateClosenessVote(
VectorGenerator(samples_a), VectorGenerator(samples_a_unsorted),
samples_a.size(), kDefaultL2Tolerance, /*granularity=*/1.0));
EXPECT_TRUE(GenerateClosenessVote(
VectorGenerator(samples_a_unsorted), VectorGenerator(samples_a),
samples_a_unsorted.size(), kDefaultL2Tolerance, /*granularity=*/1.0));
EXPECT_FALSE(GenerateClosenessVote(
VectorGenerator(samples_a), VectorGenerator(samples_b_unsorted),
samples_a.size(), kDefaultL2Tolerance, /*granularity=*/1.0));
EXPECT_FALSE(GenerateClosenessVote(
VectorGenerator(samples_a_unsorted), VectorGenerator(samples_b),
samples_a_unsorted.size(), kDefaultL2Tolerance, /*granularity=*/1.0));
EXPECT_FALSE(GenerateClosenessVote(
VectorGenerator(samples_a_unsorted), VectorGenerator(samples_b_unsorted),
samples_a_unsorted.size(), kDefaultL2Tolerance, /*granularity=*/1.0));
}
TEST(ApproximateDpVoteTest, AcceptsIdenticalSamples) {
std::vector<double> samples = {1.0, 2.0, 2.0, 3.0, 3.0, 3.0, 4.0, 4.0,
4.0, 4.0, 5.0, 5.0, 5.0, 5.0, 5.0};
// Identical sample sets should accept with epsilon and delta = 0, and almost
// any delta tolerance. We use granularity = 1.0 because the samples are
// already multiples of 1.0.
EXPECT_TRUE(GenerateApproximateDpVote(
VectorGenerator(samples), VectorGenerator(samples), samples.size(),
/*epsilon=*/0.0, /*delta=*/0.0, kLowDeltaTolerance, /*granularity=*/1.0));
EXPECT_TRUE(GenerateApproximateDpVote(
VectorGenerator(samples), VectorGenerator(samples), samples.size(),
/*epsilon=*/0.0, /*delta=*/0.0, kHighL2Tolerance, /*granularity=*/1.0));
}
TEST(ApproximateDpVoteTest, RejectsDifferentSamples) {
std::vector<double> samples_a = {1.0, 2.0, 2.0, 3.0, 3.0, 3.0, 4.0, 4.0,
4.0, 4.0, 5.0, 5.0, 5.0, 5.0, 5.0};
std::vector<double> samples_b = {1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 2.0, 2.0,
2.0, 3.0, 3.0, 3.0, 4.0, 4.0, 5.0};
// We use granularity = 1.0 because the samples are already multiples of 1.0.
EXPECT_FALSE(GenerateApproximateDpVote(
VectorGenerator(samples_a), VectorGenerator(samples_b), samples_a.size(),
kDefaultEpsilon, kDefaultDelta, kDefaultDeltaTolerance,
/*granularity=*/1.0));
EXPECT_FALSE(GenerateApproximateDpVote(
VectorGenerator(samples_a), VectorGenerator(samples_b), samples_a.size(),
kDefaultEpsilon, kDefaultDelta, kLowDeltaTolerance, /*granularity=*/1.0));
}
TEST(ApproximateDpVoteTest, AcceptsDifferentSamplesWithHighTolerance) {
std::vector<double> samples_a = {1.0, 2.0, 2.0, 3.0, 3.0, 3.0, 4.0, 4.0,
4.0, 4.0, 5.0, 5.0, 5.0, 5.0, 5.0};
std::vector<double> samples_b = {1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 2.0, 2.0,
2.0, 3.0, 3.0, 3.0, 4.0, 4.0, 5.0};
// We use granularity = 1.0 because the samples are already multiples of 1.0.
EXPECT_TRUE(GenerateApproximateDpVote(
VectorGenerator(samples_a), VectorGenerator(samples_b), samples_a.size(),
kDefaultEpsilon, kDefaultDelta, kHighDeltaTolerance,
/*granularity=*/1.0));
}
TEST(ApproximateDpVoteTest, InvariantToSampleOrder) {
std::vector<double> samples_a = {1.0, 2.0, 2.0, 3.0, 3.0, 3.0, 4.0, 4.0,
4.0, 4.0, 5.0, 5.0, 5.0, 5.0, 5.0};
std::vector<double> samples_a_unsorted = {5.0, 3.0, 2.0, 3.0, 1.0,
2.0, 5.0, 3.0, 4.0, 5.0,
5.0, 4.0, 5.0, 4.0, 4.0};
std::vector<double> samples_b = {1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 2.0, 2.0,
2.0, 3.0, 3.0, 3.0, 4.0, 4.0, 5.0};
std::vector<double> samples_b_unsorted = {4.0, 1.0, 5.0, 1.0, 4.0,
2.0, 3.0, 3.0, 1.0, 2.0,
2.0, 1.0, 3.0, 1.0, 2.0};
// Identical sample sets should accept with epsilon and delta = 0, and almost
// any delta tolerance. We use granularity = 1.0 because the samples are
// already multiples of 1.0.
EXPECT_TRUE(GenerateApproximateDpVote(
VectorGenerator(samples_a), VectorGenerator(samples_a_unsorted),
samples_a.size(),
/*epsilon=*/0.0, /*delta=*/0.0, kDefaultL2Tolerance,
/*granularity=*/1.0));
EXPECT_TRUE(GenerateApproximateDpVote(
VectorGenerator(samples_a_unsorted), VectorGenerator(samples_a),
samples_a_unsorted.size(),
/*epsilon=*/0.0, /*delta=*/0.0, kDefaultL2Tolerance,
/*granularity=*/1.0));
EXPECT_FALSE(GenerateApproximateDpVote(
VectorGenerator(samples_a), VectorGenerator(samples_b_unsorted),
samples_a.size(), kDefaultEpsilon, kDefaultDelta, kDefaultL2Tolerance,
/*granularity=*/1.0));
EXPECT_FALSE(GenerateApproximateDpVote(
VectorGenerator(samples_a_unsorted), VectorGenerator(samples_b),
samples_a_unsorted.size(), kDefaultEpsilon, kDefaultDelta,
kDefaultL2Tolerance, /*granularity=*/1.0));
EXPECT_FALSE(GenerateApproximateDpVote(
VectorGenerator(samples_a_unsorted), VectorGenerator(samples_b_unsorted),
samples_a_unsorted.size(), kDefaultEpsilon, kDefaultDelta,
kDefaultL2Tolerance, /*granularity=*/1.0));
}
TEST(RunBallotTest, AcceptsMajorityTrue) {
std::vector<bool> votes = {true, true, true, true, false, false, false};
auto vote_it = votes.begin();
std::function<bool()> vote_generator = [&vote_it]() { return *(vote_it++); };
EXPECT_TRUE(RunBallot(vote_generator, votes.size()));
}
TEST(RunBallotTest, RejectsMajorityFalse) {
std::vector<bool> votes = {true, true, true, false, false, false, false};
auto vote_it = votes.begin();
std::function<bool()> vote_generator = [&vote_it]() { return *(vote_it++); };
EXPECT_FALSE(RunBallot(vote_generator, votes.size()));
}
TEST(ReferenceLaplaceTest, HasAccurateStatisticalProperties) {
double mean = 0.0;
double variance = 2.0;
std::vector<double> samples;
for (int i = 0; i < kNumSamples; ++i) {
samples.push_back(
SampleReferenceLaplacian(mean, variance, &SecureURBG::GetInstance()));
}
EXPECT_NEAR(Mean(samples), mean, 0.1);
EXPECT_NEAR(Variance(samples), variance, 0.5);
}
TEST(ReadProtoTest, ReadProtoFromFile) {
std::istringstream proto_file(
R"(
name: "Foo"
dp_test_parameters {
epsilon: 1.09861228866810969140 # = ln(3)
delta: 0.0
delta_tolerance: 0.01125
granularity: 0.015625
}
noise_sampling_parameters {
number_of_samples: 1000000
l0_sensitivity: 1
linf_sensitivity: 1.0
epsilon: 1.09861228866810969140 # = ln(3)
raw_input: 0.0
}
)");
::testing::DistributionDpTestCase expected;
google::protobuf::TextFormat::ParseFromString(
R"pb(
name: "Foo"
dp_test_parameters {
epsilon: 1.09861228866810969140 # = ln(3)
delta: 0.0
delta_tolerance: 0.01125
granularity: 0.015625
}
noise_sampling_parameters {
number_of_samples: 1000000
l0_sensitivity: 1
linf_sensitivity: 1.0
epsilon: 1.09861228866810969140 # = ln(3)
raw_input: 0.0
}
)pb",
&expected);
std::optional<::testing::DistributionDpTestCase> test_case =
ReadProto<::testing::DistributionDpTestCase>(&proto_file);
ASSERT_TRUE(test_case.has_value());
EXPECT_THAT(test_case.value(), ::differential_privacy::base::testing::EqualsProto(expected));
}
TEST(BucketizeTest, BucketizesCorrectly) {
EXPECT_EQ(Bucketize(0.5, 0, 10, 10), 0);
EXPECT_EQ(Bucketize(5.5, 0, 10, 10), 5);
EXPECT_EQ(Bucketize(9.6, 0, 10, 10), 9);
EXPECT_EQ(Bucketize(-4.5, -5, 5, 10), 0);
EXPECT_EQ(Bucketize(4.5, -5, 5, 10), 9);
EXPECT_EQ(Bucketize(8, 0, 35, 5), 1);
EXPECT_EQ(Bucketize(20, 0, 35, 5), 2);
EXPECT_EQ(Bucketize(-5.5, -5, 5, 10), 0);
EXPECT_EQ(Bucketize(-5, -5, 5, 10), 0);
EXPECT_EQ(Bucketize(5, -5, 5, 10), 9);
EXPECT_EQ(Bucketize(5.5, -5, 5, 10), 9);
EXPECT_EQ(Bucketize(-1, -5, 5, 10), 4);
EXPECT_EQ(Bucketize(0, -5, 5, 10), 5);
EXPECT_EQ(Bucketize(1, -5, 5, 10), 6);
}
} // namespace
} // namespace differential_privacy::testing