public void TestNNBinary_NoEncryption()
{
// this test exists simply to demonstrate that we have the capability of evaluating the ML model on some test data without the homomorphic encryption scheme
// essentially, it serves to make sure the computations we will be performing on the encrypted data will be correct
double[][] testX = new double[5][];
testX[0] = new double[] { 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0 };
testX[1] = new double[] { 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0 };
testX[2] = new double[] { 1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0 };
testX[3] = new double[] { 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0 };
testX[4] = new double[] { 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0 };
bool[] testYs = { true, false, true, false, false };
var weights = NNConstants.GetNNBinaryWeights();
var biases = NNConstants.GetNNBinaryBiases();
List <double[][]> TestFFOutputs = new List <double[][]>();
List <double[][]> TestActivationInputs = new List <double[][]>();
for (int testI = 0; testI < testX.Length; testI++)
{
var FFOutputs = NNConstants.GetEmptyFFVectors(weights);
var ActivationInputs = NNConstants.GetEmptyFFVectors(weights);
for (int layer = 0; layer < weights.Length; layer++)
{
var numLayerInputs = weights[layer].Length;
var numLayerOutputs = weights[layer][0].Length;
for (int ffOutputIndex = 0; ffOutputIndex < numLayerOutputs; ffOutputIndex++)
{
double nodeOutput = 0.0;
for (int inputIndex = 0; inputIndex < numLayerInputs; inputIndex++)
{
if (layer == 0)
{
nodeOutput += testX[testI][inputIndex] * weights[layer][inputIndex][ffOutputIndex];
}
else
{
nodeOutput += FFOutputs[layer - 1][inputIndex] * weights[layer][inputIndex][ffOutputIndex];
}
}
nodeOutput += biases[layer][ffOutputIndex];
if (layer < (weights.Length - 1))
{
ActivationInputs[layer][ffOutputIndex] = nodeOutput;
nodeOutput *= nodeOutput;
}
else
{
ActivationInputs[layer][ffOutputIndex] = nodeOutput;
nodeOutput = NNConstants.Sigmoid(nodeOutput);
}
FFOutputs[layer][ffOutputIndex] = nodeOutput;
}
}
TestFFOutputs.Add(FFOutputs);
TestActivationInputs.Add(ActivationInputs);
Assert.AreEqual(FFOutputs[weights.Length - 1][0] >= 0.5, testYs[testI]);
}
}
public void TestNNBinary_Encryption()
{
double[][] testX = new double[5][];
testX[0] = new double[] { 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0 };
testX[1] = new double[] { 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0 };
testX[2] = new double[] { 1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0 };
testX[3] = new double[] { 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0 };
testX[4] = new double[] { 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0 };
bool[] testYs = { true, false, true, false, false };
// setup the encryptor and other various components
EncryptionParameters parms = new EncryptionParameters(SchemeType.CKKS);
parms.PolyModulusDegree = 32768;
parms.CoeffModulus = DefaultParams.CoeffModulus256(polyModulusDegree: 32768);
SEALContext context = SEALContext.Create(parms);
CKKSEncoder encoder = new CKKSEncoder(context);
KeyGenerator keygen = new KeyGenerator(context);
PublicKey publicKey = keygen.PublicKey;
SecretKey secretKey = keygen.SecretKey;
RelinKeys relinKeys = keygen.RelinKeys(decompositionBitCount: DefaultParams.DBCmax);
Encryptor encryptor = new Encryptor(context, publicKey);
Evaluator evaluator = new Evaluator(context);
Decryptor decryptor = new Decryptor(context, secretKey);
double scale = Math.Pow(2.0, 30);
// encrypt the input features
List <List <Ciphertext> > featureCiphers = new List <List <Ciphertext> >();
for (int i = 0; i < testX.Length; i++)
{
List <Ciphertext> curFeatureCiphers = new List <Ciphertext>();
foreach (var featureVal in testX[i])
{
List <double> featureVector = new double[] { featureVal }.ToList();
Plaintext plain = new Plaintext();
encoder.Encode(featureVal, scale, plain);
Ciphertext encrypted = new Ciphertext();
encryptor.Encrypt(plain, encrypted);
curFeatureCiphers.Add(encrypted);
}
featureCiphers.Add(curFeatureCiphers);
}
/*
*
* This represents the initial border between the client and the server
* The only data that should cross this line is the public key computed above, and the cipher texts of the ML model features
*
* */
var weights = NNConstants.GetNNBinaryWeights();
var weightsEncoded = NNConstants.GetWeightsPlaintext(encoder, scale, weights);
var biases = NNConstants.GetNNBinaryBiases();
List <Ciphertext[][]> TestFFOutputs = new List <Ciphertext[][]>();
List <Ciphertext[][]> TestActivationInputs = new List <Ciphertext[][]>();
for (int testI = 0; testI < testX.Length; testI++)
{
var FFOutputs = NNConstants.GetEmptyFFCipherVectors(weights);
var ActivationInputs = NNConstants.GetEmptyFFCipherVectors(weights);
for (int layer = 0; layer < weights.Length; layer++)
{
var numLayerInputs = weights[layer].Length;
var numLayerOutputs = weights[layer][0].Length;
for (int ffOutputIndex = 0; ffOutputIndex < numLayerOutputs; ffOutputIndex++)
{
List <Ciphertext> sumInputs = new List <Ciphertext>();
//double nodeOutput = 0.0;
for (int inputIndex = 0; inputIndex < numLayerInputs; inputIndex++)
{
if (layer == 0)
{
Ciphertext multResult = new Ciphertext();
evaluator.MultiplyPlain(featureCiphers[testI][inputIndex], weightsEncoded[layer][inputIndex][ffOutputIndex], multResult);
sumInputs.Add(multResult);
}
else
{
Ciphertext multResult = new Ciphertext();
bool success = false;
int numTries = 0;
while (!success && numTries < 4)
{
try
{
evaluator.MultiplyPlain(FFOutputs[layer - 1][inputIndex], weightsEncoded[layer][inputIndex][ffOutputIndex], multResult);
success = true;
}
catch (Exception ex)
{
evaluator.RelinearizeInplace(FFOutputs[layer - 1][inputIndex], relinKeys);
evaluator.RescaleToNextInplace(FFOutputs[layer - 1][inputIndex]);
numTries++;
if (numTries >= 4)
{
throw ex;
}
}
}
sumInputs.Add(multResult);
}
}
Ciphertext activationInput = new Ciphertext();
evaluator.AddMany(sumInputs, activationInput);
Plaintext biasPT = new Plaintext();
encoder.Encode(biases[layer][ffOutputIndex], activationInput.Scale, biasPT);
evaluator.AddPlainInplace(activationInput, biasPT);
Ciphertext activationOutput = new Ciphertext();
if (layer < (weights.Length - 1))
{
ActivationInputs[layer][ffOutputIndex] = activationInput;
bool success = false;
int numTries = 0;
while (!success && numTries < 4)
{
try
{
evaluator.Square(activationInput, activationOutput);
success = true;
}
catch (Exception ex)
{
evaluator.RelinearizeInplace(activationInput, relinKeys);
evaluator.RescaleToNextInplace(activationInput);
numTries++;
if (numTries >= 4)
{
throw ex;
}
}
}
}
else
{
ActivationInputs[layer][ffOutputIndex] = activationInput;
// note that normally, we would do a sigmoid on this - however, we cant do a sigmoid
// in the encrypted space (we are limited to simple add/multiply
// so we just copy it to the output - the client will use a >/< check to determine class
activationOutput = activationInput;
}
FFOutputs[layer][ffOutputIndex] = activationOutput;
}
}
TestFFOutputs.Add(FFOutputs);
TestActivationInputs.Add(ActivationInputs);
}
/*
*
* This represents the next border between the client and the server
* The only data that should cross this line is the encrypted output of running the ML model
* In this case, the output decrypts to the input of a sigmoid - so the client will determine class membership
* based on a > or < 0 boolean
*
* */
List <List <double> > predictions = new List <List <double> >();
for (int testI = 0; testI < TestFFOutputs.Count; testI++)
{
Plaintext plainResult = new Plaintext();
var encryptedResult = TestFFOutputs[testI][2][0];
decryptor.Decrypt(encryptedResult, plainResult);
List <double> result = new List <double>();
encoder.Decode(plainResult, result);
predictions.Add(result);
}
for (int testI = 0; testI < TestFFOutputs.Count; testI++)
{
var avgResult = predictions[testI].Average();
bool prediction = false;
if (avgResult >= 0)
{
prediction = true;
}
Assert.AreEqual(prediction, testYs[testI]);
}
}