/*
* 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.examples;
import java.io.File;
import java.io.FilenameFilter;
import java.io.IOException;
import java.nio.charset.StandardCharsets;
import java.util.regex.Matcher;
import java.util.regex.Pattern;
import org.apache.mahout.math.Arrays;
import org.deidentifier.arx.ARXAnonymizer;
import org.deidentifier.arx.ARXConfiguration;
import org.deidentifier.arx.ARXCostBenefitConfiguration;
import org.deidentifier.arx.ARXLattice.ARXNode;
import org.deidentifier.arx.ARXResult;
import org.deidentifier.arx.AttributeType.Hierarchy;
import org.deidentifier.arx.Data;
import org.deidentifier.arx.DataHandle;
import org.deidentifier.arx.DataSelector;
import org.deidentifier.arx.DataSubset;
import org.deidentifier.arx.criteria.ProfitabilityJournalist;
import org.deidentifier.arx.io.CSVHierarchyInput;
import org.deidentifier.arx.metric.Metric;
import org.deidentifier.arx.metric.v2.MetricSDNMPublisherPayout;
import org.deidentifier.arx.metric.v2.QualityMetadata;
/**
* Examples of using the game-theoretic approach for
* performing a monetary cost/benefit analysis using journalist risk.
*
* @author Fabian Prasser
*/
public class Example52 extends Example {
/**
* Loads a dataset from disk
* @param dataset
* @return
* @throws IOException
*/
public static Data createData(final String dataset) throws IOException {
Data data = Data.create("data/" + dataset + ".csv", StandardCharsets.UTF_8, ';');
// Read generalization hierarchies
FilenameFilter hierarchyFilter = new FilenameFilter() {
@Override
public boolean accept(File dir, String name) {
if (name.matches(dataset + "_hierarchy_(.)+.csv")) {
return true;
} else {
return false;
}
}
};
// Create definition
File testDir = new File("data/");
File[] genHierFiles = testDir.listFiles(hierarchyFilter);
Pattern pattern = Pattern.compile("_hierarchy_(.*?).csv");
for (File file : genHierFiles) {
Matcher matcher = pattern.matcher(file.getName());
if (matcher.find()) {
CSVHierarchyInput hier = new CSVHierarchyInput(file, StandardCharsets.UTF_8, ';');
String attributeName = matcher.group(1);
data.getDefinition().setAttributeType(attributeName, Hierarchy.create(hier.getHierarchy()));
}
}
return data;
}
/**
* Entry point.
*
* @param args the arguments
* @throws IOException
*/
public static void main(String[] args) throws IOException {
// Load the dataset
Data data = createData("adult");
// We select all records with "sex=Male" to serve as the dataset which we will de-identify.
// The overall dataset serves as our population table.
DataSubset subset = DataSubset.create(data, DataSelector.create(data).field("sex").equals("Male"));
// Config from PLOS|ONE paper
solve(data, ARXCostBenefitConfiguration.create()
.setAdversaryCost(4d)
.setAdversaryGain(300d)
.setPublisherLoss(300d)
.setPublisherBenefit(1200d), subset);
// Larger publisher loss
solve(data, ARXCostBenefitConfiguration.create()
.setAdversaryCost(4d)
.setAdversaryGain(300d)
.setPublisherLoss(600d)
.setPublisherBenefit(1200d), subset);
// Even larger publisher loss
solve(data, ARXCostBenefitConfiguration.create()
.setAdversaryCost(4d)
.setAdversaryGain(300d)
.setPublisherLoss(1200d)
.setPublisherBenefit(1200d), subset);
// Larger publisher loss and less adversary costs
solve(data, ARXCostBenefitConfiguration.create()
.setAdversaryCost(2d)
.setAdversaryGain(300d)
.setPublisherLoss(600d)
.setPublisherBenefit(1200d), subset);
}
/**
* Solve the privacy problem
* @param data
* @param config
* @param subset
* @throws IOException
*/
private static void solve(Data data, ARXCostBenefitConfiguration config, DataSubset subset) throws IOException {
// Release
data.getHandle().release();
// Configure
ARXConfiguration arxconfig = ARXConfiguration.create();
arxconfig.setCostBenefitConfiguration(config);
// Create model for measuring publisher's benefit
MetricSDNMPublisherPayout maximizePublisherPayout = Metric.createPublisherPayoutMetric(true);
// Create privacy model for the game-theoretic approach
ProfitabilityJournalist profitability = new ProfitabilityJournalist(subset);
// Configure ARX
arxconfig.setMaxOutliers(1d);
arxconfig.setQualityModel(maximizePublisherPayout);
arxconfig.addPrivacyModel(profitability);
// Anonymize
ARXAnonymizer anonymizer = new ARXAnonymizer();
ARXResult result = anonymizer.anonymize(data, arxconfig);
ARXNode node = result.getGlobalOptimum();
DataHandle handle = result.getOutput(node, false).getView();
// Print stuff
System.out.println("Data: " + data.getHandle().getView().getNumRows() + " records with " + data.getDefinition().getQuasiIdentifyingAttributes().size() + " quasi-identifiers");
System.out.println(" - Configuration: " + config.toString());
System.out.println(" - Policies available: " + result.getLattice().getSize());
System.out.println(" - Solution: " + Arrays.toString(node.getTransformation()));
System.out.println(" * Optimal: " + result.getLattice().isComplete());
System.out.println(" * Time needed: " + result.getTime() + "[ms]");
for (QualityMetadata<?> metadata : node.getLowestScore().getMetadata()) {
System.out.println(" * " + metadata.getParameter() + ": " + metadata.getValue());
}
System.out.println(" * Suppressed records: " + handle.getStatistics().getEquivalenceClassStatistics().getNumberOfOutlyingTuples());
}
}