/*
* ARX: Powerful Data Anonymization
* Copyright 2012 - 2017 Fabian Prasser, Florian Kohlmayer and contributors
*
* 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.
*/
package org.deidentifier.arx.test;
import java.util.Arrays;
import java.util.Collection;
import org.deidentifier.arx.ARXConfiguration;
import org.deidentifier.arx.ARXPopulationModel;
import org.deidentifier.arx.ARXPopulationModel.Region;
import org.deidentifier.arx.ARXSolverConfiguration;
import org.deidentifier.arx.criteria.AverageReidentificationRisk;
import org.deidentifier.arx.criteria.PopulationUniqueness;
import org.deidentifier.arx.criteria.SampleUniqueness;
import org.deidentifier.arx.metric.Metric;
import org.deidentifier.arx.risk.RiskModelPopulationUniqueness.PopulationUniquenessModel;
import org.junit.runner.RunWith;
import org.junit.runners.Parameterized;
import org.junit.runners.Parameterized.Parameters;
/**
* Test for risk-based anonymization
*
* @author Fabian Prasser
*/
@RunWith(Parameterized.class)
public class TestAnonymizationRiskBased extends AbstractAnonymizationTest {
/**
* Returns the test cases.
*
* @return
*/
@Parameters(name = "{index}:[{0}]")
public static Collection<Object[]> cases() {
return Arrays.asList(new Object[][] {
/* 0 */{ new ARXAnonymizationTestCase(ARXConfiguration.create(0.04d, Metric.createPrecomputedEntropyMetric(0.1d)).addPrivacyModel(new AverageReidentificationRisk(0.01d)), "./data/adult.csv", 314637.8461904862, new int[] { 1, 0, 1, 1, 3, 2, 2, 1, 1 }, false) },
{ new ARXAnonymizationTestCase(ARXConfiguration.create(0.04d, Metric.createPrecomputedLossMetric(0.1d)).addPrivacyModel(new SampleUniqueness(0.01d)), "./data/adult.csv", 0.1606952725863784, new int[] { 0, 3, 0, 0, 1, 1, 1, 1, 0 }, false) },
{ new ARXAnonymizationTestCase(ARXConfiguration.create(0.04d, Metric.createPrecomputedEntropyMetric(0.1d)).addPrivacyModel(getPopulationUniqueness(0.0001d, PopulationUniquenessModel.DANKAR)), "./data/adult.csv", 144298.1603344462, new int[] { 0, 0, 1, 1, 1, 2, 1, 0, 0 }, false) },
{ new ARXAnonymizationTestCase(ARXConfiguration.create(0.04d, Metric.createPrecomputedLossMetric(0.1d)).addPrivacyModel(getPopulationUniqueness(0.0001d, PopulationUniquenessModel.ZAYATZ)), "./data/adult.csv", 0.16078200456326086, new int[] { 0, 3, 0, 0, 1, 1, 1, 1, 0 }, false) },
{ new ARXAnonymizationTestCase(ARXConfiguration.create(0.04d, Metric.createPrecomputedEntropyMetric(0.1d)).addPrivacyModel(getPopulationUniqueness(0.0001d, PopulationUniquenessModel.PITMAN)), "./data/adult.csv", 144298.1603344462, new int[] { 0, 0, 1, 1, 1, 2, 1, 0, 0 }, false) },
{ new ARXAnonymizationTestCase(ARXConfiguration.create(0.04d, Metric.createPrecomputedLossMetric(0.1d)).addPrivacyModel(getPopulationUniqueness(0.0001d, PopulationUniquenessModel.SNB)), "./data/adult.csv", 0.17599055898432758, new int[] { 0, 3, 0, 0, 2, 1, 1, 1, 0 }, false) }
});
}
/**
* Constructor
* @param testCase
*/
public TestAnonymizationRiskBased(final ARXAnonymizationTestCase testCase) {
super(testCase);
}
/**
* Returns a privacy model for population uniquqeness
* @param threshold
* @param model
* @return
*/
private static PopulationUniqueness getPopulationUniqueness(double threshold, PopulationUniquenessModel model) {
return new PopulationUniqueness(threshold, model,
ARXPopulationModel.create(Region.USA),
ARXSolverConfiguration.create()
.setDeterministic(true)
.iterationsPerTry(15));
}
}