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Credit card fraud mlhelib 2 0 (#67)
* pointing repo to new version of helib * delted a bynch of cmake stuff i added * updating the cmakelists file to reflect the 2.0.0 build * took some work but made helib build for macos * working through the build errors in xocde to get this working * made some changes to the .h files to handle some conversion types. wnat to check this in separate so its not conflicting with the main repo in case I need to make these changes up stream * removed arm64 from a excluded arch * implemented the new Privacy Preserving Search example using MLHelib and helib 2.0. its rough and the old code has been commented out as well as not fully implemented the UI yet, but its working * fixed the csv parsing to not split up the capital if there was a , in there * removed the old commented code and made it prettier * removed the old json file that is not being used in this anymore * removed the old aihelib and then imported mlhelib. Wrapping the clientServer class in an obj-c class * Continuing to build the wrapper, and passing the resource path to the C class seems to work. It loads the h5 file * server and client are running although there is a race condition where it looks like its reading the file before its done writing it, and something is off in the settings * had to separate out the CreditSampleData struct into its own .h file so we can import into c++ files, and switched NSintegrer to int * added the populating of the struct with the data to pass back to the view * removed old code * applying copyright headers * change dpeloyment target for helib to be 10.14 and the dependency directory path to include HElib/dependency to point the json lib file to the right path * upping the os version support for the toolkit to be 10.14 * changed the copy, fixing links and adding a minimum verison in the Getting Started doc * removing a .h file that accdientally was checked in and not deleted
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‎Credit Card Fraud/ClientServer/ClientServer.cpp

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/*
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* MIT License
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*
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* Copyright (c) 2020 International Business Machines
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*
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* Permission is hereby granted, free of charge, to any person obtaining a copy
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* of this software and associated documentation files (the "Software"), to deal
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* in the Software without restriction, including without limitation the rights
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* to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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* copies of the Software, and to permit persons to whom the Software is
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* furnished to do so, subject to the following conditions:
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*
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* The above copyright notice and this permission notice shall be included in all
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* copies or substantial portions of the Software.
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*
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* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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* SOFTWARE.
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*/
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//
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// INFO: The API used to create these examples can be found online
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// ML-HElib: https://ibm.github.io/fhe-toolkit-linux”
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// HElib: https://ibm.github.io/fhe-toolkit-linux/html/helib/index.html
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//
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#include <iostream>
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#include <iomanip>
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#include "ClientServer.h"
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#include "helayers/simple_nn/SimpleNeuralNetPlain.h"
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#include "helayers/simple_nn/SimpleNeuralNet.h"
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#include "helayers/simple_nn/CipherMatrixEncoder.h"
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using namespace std;
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using namespace helayers;
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const string outDir = getExamplesOutputDir();
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const string clientContext = outDir + "/client_context.bin";
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const string serverContext = outDir + "/server_context.bin";
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const string encryptedModelFile = outDir + "/encrypted_model.bin";
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// paths from which to load the plain model, samples and labels
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const string plainModelFile = "/model_42098.h5";
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const string plainSamplesFile = "/x_test.h5";
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const string plainLabelsFile = "/y_test.h5";
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double classificationThreshold =
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0.5; // used to separate positive from negative samples
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// Client methods
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Client::Client(const string& dataDir) : currentBatch(0), dataDir(dataDir) {}
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Client::~Client() {}
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void Client::init()
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{
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cout << "CLIENT: loading client side context . . ." << endl;
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he = HeContext::loadHeContextFromFile(clientContext);
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he->printSignature(cout);
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batchSize = he->slotCount();
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cout << "CLIENT: loading plain model . . ." << endl;
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SimpleNeuralNetPlain plainNet;
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H5Parser parser(dataDir + plainModelFile);
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plainNet.loadh5(parser,
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std::vector<string>{"dense_1", "dense_2", "dense_3"},
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std::vector<int>{29, 20, 5, 1},
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batchSize);
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cout << "CLIENT: encrypting plain model . . ." << endl;
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SimpleNeuralNet netHe(*he);
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netHe.initFromNet(plainNet);
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cout << "CLIENT: saving encrypted model . . ." << endl;
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ofstream ofs(encryptedModelFile, ios::out | ios::binary);
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netHe.save(ofs);
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ofs.close();
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cout << "CLIENT: loading plain samples . . ." << endl;
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TrainingSetPlain tmpTs(batchSize);
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tmpTs.loadFromH5(dataDir + plainSamplesFile,
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"x_test",
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dataDir + plainLabelsFile,
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"y_test");
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numBatches = tmpTs.getNumBatches();
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ts = make_shared<TrainingSetPlain>(tmpTs);
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cout << "Number of samples: " << ts->getNumSamples() << endl;
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cout << "Batch size: " << batchSize << endl;
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cout << "Number of batches: " << numBatches << endl;
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}
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void Client::encryptAndSaveSamples(int batch,
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const string& encryptedSamplesFile) const
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{
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const CipherMatrixEncoder encoder(*he);
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cout << "CLIENT: encrypting plain samples . . ." << endl;
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HELAYERS_TIMER_PUSH("data-encrypt");
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const DoubleMatrixArray& plainSamples = ts->getSamples(batch);
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CipherMatrix encryptedSamples(*he);
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encoder.encodeEncrypt(encryptedSamples, plainSamples.getTensor());
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//HELAYERS_TIMER_POP();
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cout << "CLIENT: saving encrypted samples . . ." << endl;
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ofstream ofs(encryptedSamplesFile, ios::out | ios::binary);
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encryptedSamples.save(ofs);
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ofs.close();
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}
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void Client::decryptPredictions(const string& encryptedPredictionsFile)
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{
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CipherMatrixEncoder encoder(*he);
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encoder.getEncoder().setDecryptAddedNoiseEnabled(false);
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cout << "CLIENT: loading encrypted predictions . . ." << endl;
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CipherMatrix encryptedPredictions(*he);
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ifstream ifs(encryptedPredictionsFile, ios::in | ios::binary);
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encryptedPredictions.load(ifs);
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ifs.close();
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cout << "CLIENT: decrypting predictions . . ." << endl;
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HELAYERS_TIMER_PUSH("data-decrypt");
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DoubleMatrixArray plainPredictions(
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encoder.decryptDecodeDouble(encryptedPredictions));
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allPredictions.push_back(plainPredictions);
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HELAYERS_TIMER_POP();
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}
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void Client::assessResults(CreditSampleResults* creditData)
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{
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cout << "CLIENT: assessing results so far . . ." << endl;
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int truePositives = 0;
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int trueNegatives = 0;
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int falsePositives = 0;
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int falseNegatives = 0;
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currentBatch++;
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// go over each batch and count hits
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for (int i = 0; i < currentBatch; ++i) {
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const DoubleMatrixArray& labels = ts->getLabels(i);
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const DoubleMatrixArray& predictions = allPredictions.at(i);
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size_t samplesToCheck = labels.size();
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if (i == numBatches - 1) // last batch may partially be populated with
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// "dummy" labels to ignore
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samplesToCheck = ts->getNumSamples() - (batchSize * (numBatches - 1));
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for (int j = 0; j < samplesToCheck; ++j) {
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int label = labels.getMat(j).get(0, 0);
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int classification =
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(predictions.getMat(j).get(0, 0) > classificationThreshold ? 1 : 0);
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if (classification == label && classification == 1)
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truePositives++;
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else if (classification == label && classification == 0)
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trueNegatives++;
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else if (classification != label && classification == 1)
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falsePositives++;
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else
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falseNegatives++;
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}
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}
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double precision = ((double)truePositives / (truePositives + falsePositives));
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double recall = ((double)truePositives / (truePositives + falseNegatives));
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double f1Score = (2 * precision * recall) / (precision + recall);
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cout << endl;
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cout << "|---------------------------------------------|" << endl;
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cout << "| | True condition |" << endl;
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cout << "| ----------------------|" << endl;
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cout << "| | Positive | Negative |" << endl;
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cout << "|---------------------------------------------|" << endl;
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cout << "| Predicted | Positive |" << setw(8) << truePositives << " |"
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<< setw(8) << falsePositives << " |" << endl;
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cout << "| |--------------------------------|" << endl;
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cout << "| condition | Negative |" << setw(8) << falseNegatives << " |"
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<< setw(8) << trueNegatives << " |" << endl;
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cout << "|---------------------------------------------|" << endl;
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cout << endl;
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cout << "Precision: " << precision << endl;
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cout << "Recall: " << recall << endl;
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cout << "F1 score: " << f1Score << endl;
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creditData->f1Score = f1Score;
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creditData->recall = recall;
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creditData->precision = precision;
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creditData->truePositives = truePositives;
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creditData->trueNegatives = trueNegatives;
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creditData->falsePositives = falsePositives;
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creditData->falseNegatives = falseNegatives;
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}
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// Server methods
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Server::~Server() {}
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void Server::init()
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{
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cout << "SERVER: loading server side context . . ." << endl;
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he = HeContext::loadHeContextFromFile(serverContext);
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he->printSignature(cout);
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cout << "SERVER: loading encrypted model . . ." << endl;
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ifstream ifs(encryptedModelFile, ios::in | ios::binary);
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SimpleNeuralNet net(*he);
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net.load(ifs);
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ifs.close();
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encryptedNet = make_shared<SimpleNeuralNet>(net);
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}
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void Server::processEncryptedSamples(
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const string& encryptedSamplesFile,
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const string& encryptedPredictionsFile) const
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{
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const CipherMatrixEncoder encoder(*he);
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cout << "SERVER: loading encrypted samples . . ." << endl;
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CipherMatrix encryptedSamples(*he);
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ifstream ifs(encryptedSamplesFile, ios::in | ios::binary);
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encryptedSamples.load(ifs);
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ifs.close();
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cout << "SERVER: predicting over encrypted samples . . ." << endl;
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CipherMatrix encryptedPredictions(*he);
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encryptedNet->predict(encryptedSamples, encryptedPredictions);
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cout << "SERVER: saving encrypted predictions . . ." << endl;
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ofstream ofs(encryptedPredictionsFile, ios::out | ios::binary);
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encryptedPredictions.save(ofs);
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ofs.close();
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}

‎Credit Card Fraud/ClientServer/ClientServer.h

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/*
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* MIT License
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*
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* Copyright (c) 2020 International Business Machines
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*
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* Permission is hereby granted, free of charge, to any person obtaining a copy
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* of this software and associated documentation files (the "Software"), to deal
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* in the Software without restriction, including without limitation the rights
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* to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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* copies of the Software, and to permit persons to whom the Software is
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* furnished to do so, subject to the following conditions:
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*
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* The above copyright notice and this permission notice shall be included in all
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* copies or substantial portions of the Software.
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*
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* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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* SOFTWARE.
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*/
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#ifndef EXAMPLES_NNFRAUD_CLIENTSERVER_H
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#define EXAMPLES_NNFRAUD_CLIENTSERVER_H
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#include "helayers/hebase/hebase.h"
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#include "helayers/simple_nn/SimpleNeuralNet.h"
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#include "helayers/simple_nn/TrainingSetPlain.h"
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#include "CreditSampleResults.h"
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/// A class representing the client side
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class Client
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{
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std::shared_ptr<helayers::HeContext> he;
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std::shared_ptr<helayers::TrainingSetPlain> ts;
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std::vector<helayers::DoubleMatrixArray> allPredictions;
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int numBatches;
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int batchSize;
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int currentBatch;
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const std::string& dataDir;
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public:
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/// Construct a client.
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/// @param[in] dataDir folder where input data is
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Client(const std::string& dataDir);
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~Client();
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/// Initialize: Load he context, load network, load training set,
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/// Encrypt network and save it to file to be sent to server.
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void init();
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/// Encrypt a batch of samples and save to file to be sent to server.
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/// @param[in] batch Batch number
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/// @param[in] encryptedSamplesFile File name to write to
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void encryptAndSaveSamples(int batch,
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const std::string& encryptedSamplesFile) const;
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/// Loads a batch of predictions from file, decrypt them, and store results
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/// in a member for assessment.
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/// @param[in] encryptePredictionsFile File name to read from
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void decryptPredictions(const std::string& encryptedPredictionsFile);
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/// Assess received predictions compared with training set's labels (the
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/// ground truth).
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void assessResults(CreditSampleResults* creditData);
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/// Total number of batches in training set.
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int getNumBatches() const { return numBatches; }
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};
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/// A class representing the server side
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class Server
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{
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std::shared_ptr<helayers::HeContext> he;
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std::shared_ptr<helayers::SimpleNeuralNet> encryptedNet;
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public:
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~Server();
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void init();
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void processEncryptedSamples(
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const std::string& encryptedSamplesFile,
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const std::string& encryptedPredictionsFile) const;
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};
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#endif /* EXAMPLES_NNFRAUD_CLIENTSERVER_H */

‎Credit Card Fraud/ClientServer/ClientWrapper.h

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/*
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* MIT License
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*
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* Copyright (c) 2020 International Business Machines
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*
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* Permission is hereby granted, free of charge, to any person obtaining a copy
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* of this software and associated documentation files (the "Software"), to deal
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* in the Software without restriction, including without limitation the rights
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* to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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* copies of the Software, and to permit persons to whom the Software is
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* furnished to do so, subject to the following conditions:
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*
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* The above copyright notice and this permission notice shall be included in all
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* copies or substantial portions of the Software.
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*
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* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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* SOFTWARE.
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*/
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#import <Foundation/Foundation.h>
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#import "CreditSampleResultsData.h"
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#import "CreditSampleResults.h"
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NS_ASSUME_NONNULL_BEGIN
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@interface ClientWrapper : NSObject
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- (int)getNumBatches;
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- (void)encrypt:(int)batch andSaveSamples:(NSString *)encryptedSamplesFile;
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- (void)decryptPredictions:(NSString *)encryptedPredictionsFile;
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- (void)assessResults:(CreditSampleResults *)creditSampleResults;
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@end
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NS_ASSUME_NONNULL_END

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