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* Licensed to the Apache Software Foundation (ASF) under one
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* distributed with this work for additional information
* regarding copyright ownership. The ASF licenses this file
* to you 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
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* KIND, either express or implied. See the License for the
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package org.apache.sysds.test.functions.privacy;
import org.apache.sysds.parser.DataExpression;
import org.apache.sysds.runtime.meta.MatrixCharacteristics;
import org.apache.sysds.runtime.privacy.PrivacyConstraint;
import org.apache.sysds.runtime.privacy.finegrained.DataRange;
import org.apache.sysds.test.AutomatedTestBase;
import org.apache.sysds.test.TestConfiguration;
import org.junit.Assert;
import org.junit.Test;
public class PrivacyLineageTest extends AutomatedTestBase {
private static final String TEST_DIR = "functions/privacy/";
private final static String TEST_CLASS_DIR = TEST_DIR + PrivacyLineageTest.class.getSimpleName() + "/";
@Override public void setUp() {
addTestConfiguration("LineageReuse",
new TestConfiguration(TEST_CLASS_DIR, "PrivacyLineageTest", new String[]{"c"}));
}
@Test
public void propagatePrivacyWithLineageFullReuseTest() {
propagationWithLineage(PrivacyConstraint.PrivacyLevel.PrivateAggregation);
}
private void propagationWithLineage(PrivacyConstraint.PrivacyLevel privacyLevel) {
TestConfiguration config = availableTestConfigurations.get("LineageReuse");
loadTestConfiguration(config);
fullDMLScriptName = SCRIPT_DIR + TEST_DIR + config.getTestScript() + ".dml";
int n = 20;
int m = 20;
double[][] a = getRandomMatrix(m, n, -1, 1, 1, -1);
int k = 20;
double[][] b = getRandomMatrix(n, k, -1, 1, 1, -1);
PrivacyConstraint privacyConstraint = new PrivacyConstraint(privacyLevel);
privacyConstraint.getFineGrainedPrivacy().put(new DataRange(new long[]{0,0}, new long[]{4,4}), PrivacyConstraint.PrivacyLevel.Private);
MatrixCharacteristics aCharacteristics = new MatrixCharacteristics(m, n, k, k);
MatrixCharacteristics bCharacteristics = new MatrixCharacteristics(n, k, k, k);
writeInputMatrixWithMTD("A", a, false, aCharacteristics, privacyConstraint);
writeInputMatrixWithMTD("B", b, false, bCharacteristics);
programArgs = new String[]{"-lineage", "reuse_full", "-nvargs",
"A=" + input("A"), "B=" + input("B"), "C=" + output("C")};
runTest(true,false,null,-1);
finegrainedAssertions();
programArgs = new String[]{"-nvargs",
"A=" + input("A"), "B=" + input("B"), "C=" + output("C")};
runTest(true,false,null,-1);
finegrainedAssertions();
}
private static void finegrainedAssertions(){
String outputFineGrained = readDMLMetaDataValueCatchException("C", OUTPUT_DIR, DataExpression.FINE_GRAINED_PRIVACY);
Assert.assertEquals(
"{\"Private\":[[[0,0],[0,19]],[[1,0],[1,19]],[[2,0],[2,19]],[[3,0],[3,19]],[[4,0],[4,19]]],\"PrivateAggregation\":[]}",
outputFineGrained);
}
}