def do_per_amount(amount, subtract_from=15): """ Called on every message in the stream """ print("Transaction amount ", amount) parms = EncryptionParameters() parms.set_poly_modulus("1x^2048 + 1") parms.set_coeff_modulus(seal.coeff_modulus_128(2048)) parms.set_plain_modulus(1 << 8) context = SEALContext(parms) # Encode encoder = FractionalEncoder(context.plain_modulus(), context.poly_modulus(), 64, 32, 3) # To create a fresh pair of keys one can call KeyGenerator::generate() at any time. keygen = KeyGenerator(context) public_key = keygen.public_key() secret_key = keygen.secret_key() encryptor = Encryptor(context, public_key) plain1 = encoder.encode(amount) encoded2 = encoder.encode(subtract_from) # Encrypt encrypted1 = Ciphertext(parms) encryptor.encrypt(plain1, encrypted1) # Evaluate evaluator = Evaluator(context) evaluated = evaluate_subtraction_from_plain(evaluator, encrypted1, encoded2) # Decrypt and decode decryptor = Decryptor(context, secret_key) plain_result = Plaintext() decryptor.decrypt(evaluated, plain_result) result = encoder.decode(plain_result) str_result = "Amount left = " + str(result) print(str_result) return str_result
def evaluate(self, encrypted_data, lower_idx=0, higher_idx=-1): result = Ciphertext() # Setup the encoder encoder = FractionalEncoder(self.context.plain_modulus(), self.context.poly_modulus(), 64, 32, 3) # Unpack the data first unpacked_data = [] for d in encrypted_data[lower_idx:higher_idx]: unpacked_data.append(pickle.loads(base64.b64decode(d))) # Perform operations self.evaluator.add_many(unpacked_data, result) div = encoder.encode(1/len(unpacked_data)) self.evaluator.multiply_plain(result, div) # Pack the result result = base64.b64encode( pickle.dumps(result) ) return result
def encrypt(self, data): if not self.initialized: self.log("Not initialized") return False # Setup the encoder encoder = FractionalEncoder(self.context.plain_modulus(), self.context.poly_modulus(), 64, 32, 3) # Create the array of encrypted data objects encrypted_data = [] for raw_data in data: encrypted_data.append(Ciphertext(self.encrypt_params)) self.encryptor.encrypt( encoder.encode(raw_data), encrypted_data[-1] ) # Pickle each Ciphertext, base64 encode it, and store it in the array for i in range(0, len(encrypted_data)): encrypted_data[i].save("fhe_enc.bin") encrypted_data[i] = base64.b64encode( pickle.dumps(encrypted_data[i]) ) return encrypted_data
def test1(): #TODO make the function taking dir name, iterating through all the descriptors, encrypting #TODO add progress bar - the videos are decrypted #TODO make a function taking a query and passing it to the sklearn function KNN with custom similarity funtion #TODO add progress bar - the search is performed (calculate distance between encrypted vectors) #TODO return results #TODO Vlad Display. The results are true. The same as we would compute similarity between ncrypted vectors. # In the same time we did not have to decrypt vectors stored in 3rd parity service to perform a search. #TODO get the metrics #TODO how to opimize a = np.ones((1, DESC_SIZE)) b = np.ones((1, DESC_SIZE)) for i in range(a.shape[1]): a[0, i] = random.uniform(0.0, 1.0) b[0, i] = random.uniform(0.0, 1.0) print("Descriptors for images a and b are: ") print("a ", a) print("b ", b) context, params = config() public_key, secret_key = keygen(context) frac_encoder = FractionalEncoder(context.plain_modulus(), context.poly_modulus(), 64, 32, 3) encryptor = Encryptor(context, public_key) evaluator = Evaluator(context) decryptor = Decryptor(context, secret_key) e1 = encrypt_vec(params, encryptor, frac_encoder, a) e3 = encrypt_vec(params, encryptor, frac_encoder, b) print("Calculate distance between plain a and b:") sim = euclidean_dist(a, b) print("Similarity between a and b: {}".format(sim)) print("Calculating distance between encrypted a and b:") time_start = time.time() enc_res = euclidean_dist_enc(evaluator, e1, e3) time_end = time.time() print("Done. Time: {} miliseconds".format( (str)(1000 * (time_end - time_start)))) plain_result = Plaintext() print("Decrypting result: ") time_start = time.time() decryptor.decrypt(enc_res, plain_result) res = frac_encoder.encode(plain_result) time_end = time.time() print("Done. Time: {} miliseconds".format( (str)(1000 * (time_end - time_start)))) print( "........................................................................" ) print("Noise budget {} bits".format( decryptor.invariant_noise_budget(enc_res))) print("Decrypted result {}, original result {}".format(res, sim)) print("Calculate distance between plain a and a:") sim = euclidean_dist(a, a.copy()) print("Similarity between a and b: {}".format(sim)) print("Calculating distance between encrypted a and a:") time_start = time.time() enc_res = euclidean_dist_enc(evaluator, e1, e1) time_end = time.time() print("Done. Time: {} miliseconds".format( (str)(1000 * (time_end - time_start)))) plain_result = Plaintext() print("Decrypting result: ") time_start = time.time() decryptor.decrypt(enc_res, plain_result) res = frac_encoder.encode(plain_result) time_end = time.time() print("Done. Time: {} miliseconds".format( (str)(1000 * (time_end - time_start)))) print( "........................................................................." ) print("Noise budget {} bits".format( decryptor.invariant_noise_budget(enc_res))) print("Decrypted result {}, original result {}".format(res, sim))
def dot_product(): print("Example: Weighted Average") # In this example we demonstrate the FractionalEncoder, and use it to compute # a weighted average of 10 encrypted rational numbers. In this computation we # perform homomorphic multiplications of ciphertexts by plaintexts, which is # much faster than regular multiplications of ciphertexts by ciphertexts. # Moreover, such `plain multiplications' never increase the ciphertext size, # which is why we have no need for evaluation keys in this example. # We start by creating encryption parameters, setting up the SEALContext, keys, # and other relevant objects. Since our computation has multiplicative depth of # only two, it suffices to use a small poly_modulus. parms = EncryptionParameters() parms.set_poly_modulus("1x^2048 + 1") parms.set_coeff_modulus(seal.coeff_modulus_128(2048)) parms.set_plain_modulus(1 << 8) context = SEALContext(parms) print_parameters(context) keygen = KeyGenerator(context) keygen2 = KeyGenerator(context) public_key = keygen.public_key() secret_key = keygen.secret_key() secret_key2 = keygen.secret_key() # We also set up an Encryptor, Evaluator, and Decryptor here. encryptor = Encryptor(context, public_key) evaluator = Evaluator(context) decryptor = Decryptor(context, secret_key2) # Create a vector of 10 rational numbers (as doubles). # rational_numbers = [3.1, 4.159, 2.65, 3.5897, 9.3, 2.3, 8.46, 2.64, 3.383, 2.795] rational_numbers = np.random.rand(10) # Create a vector of weights. # coefficients = [0.1, 0.05, 0.05, 0.2, 0.05, 0.3, 0.1, 0.025, 0.075, 0.05] coefficients = np.random.rand(10) my_result = np.dot(rational_numbers, coefficients) # We need a FractionalEncoder to encode the rational numbers into plaintext # polynomials. In this case we decide to reserve 64 coefficients of the # polynomial for the integral part (low-degree terms) and expand the fractional # part to 32 digits of precision (in base 3) (high-degree terms). These numbers # can be changed according to the precision that is needed; note that these # choices leave a lot of unused space in the 2048-coefficient polynomials. encoder = FractionalEncoder(context.plain_modulus(), context.poly_modulus(), 64, 32, 3) # We create a vector of ciphertexts for encrypting the rational numbers. encrypted_rationals = [] rational_numbers_string = "Encoding and encrypting: " for i in range(10): # We create our Ciphertext objects into the vector by passing the # encryption parameters as an argument to the constructor. This ensures # that enough memory is allocated for a size 2 ciphertext. In this example # our ciphertexts never grow in size (plain multiplication does not cause # ciphertext growth), so we can expect the ciphertexts to remain in the same # location in memory throughout the computation. In more complicated examples # one might want to call a constructor that reserves enough memory for the # ciphertext to grow to a specified size to avoid costly memory moves when # multiplications and relinearizations are performed. encrypted_rationals.append(Ciphertext(parms)) encryptor.encrypt(encoder.encode(rational_numbers[i]), encrypted_rationals[i]) rational_numbers_string += (str)(rational_numbers[i])[:6] if i < 9: rational_numbers_string += ", " print(rational_numbers_string) # Next we encode the coefficients. There is no reason to encrypt these since they # are not private data. encoded_coefficients = [] encoded_coefficients_string = "Encoding plaintext coefficients: " encrypted_coefficients =[] for i in range(10): encoded_coefficients.append(encoder.encode(coefficients[i])) encrypted_coefficients.append(Ciphertext(parms)) encryptor.encrypt(encoded_coefficients[i], encrypted_coefficients[i]) encoded_coefficients_string += (str)(coefficients[i])[:6] if i < 9: encoded_coefficients_string += ", " print(encoded_coefficients_string) # We also need to encode 0.1. Multiplication by this plaintext will have the # effect of dividing by 10. Note that in SEAL it is impossible to divide # ciphertext by another ciphertext, but in this way division by a plaintext is # possible. div_by_ten = encoder.encode(0.1) # Now compute each multiplication. prod_result = [Ciphertext() for i in range(10)] prod_result2 = [Ciphertext() for i in range(10)] print("Computing products: ") for i in range(10): # Note how we use plain multiplication instead of usual multiplication. The # result overwrites the first argument in the function call. evaluator.multiply_plain(encrypted_rationals[i], encoded_coefficients[i], prod_result[i]) evaluator.multiply(encrypted_rationals[i], encrypted_coefficients[i], prod_result2[i]) print("Done") # To obtain the linear sum we need to still compute the sum of the ciphertexts # in encrypted_rationals. There is an easy way to add together a vector of # Ciphertexts. encrypted_result = Ciphertext() encrypted_result2 = Ciphertext() print("Adding up all 10 ciphertexts: ") evaluator.add_many(prod_result, encrypted_result) evaluator.add_many(prod_result2, encrypted_result2) print("Done") # Perform division by 10 by plain multiplication with div_by_ten. # print("Dividing by 10: ") # evaluator.multiply_plain(encrypted_result, div_by_ten) # print("Done") # How much noise budget do we have left? print("Noise budget in result: " + (str)(decryptor.invariant_noise_budget(encrypted_result)) + " bits") # Decrypt, decode, and print result. plain_result = Plaintext() plain_result2 = Plaintext() print("Decrypting result: ") decryptor.decrypt(encrypted_result, plain_result) decryptor.decrypt(encrypted_result2, plain_result2) print("Done") result = encoder.decode(plain_result) print("Weighted average: " + (str)(result)[:8]) result2 = encoder.decode(plain_result2) print("Weighted average: " + (str)(result2)[:8]) print('\n\n', my_result)
class FractionalEncoderUtils: def __init__(self, context: FracContext): """ Class providing encoding and encryption operations, operations over encrypted data :param context: Context initialising HE parameters """ self.context = context.context self.public_key = context.public_key self.encryptor = Encryptor(self.context, self.public_key) self.encoder = FractionalEncoder(self.context.plain_modulus(), self.context.poly_modulus(), 64, 32, 3) self.evaluator = context.evaluator self.ev_keys = EvaluationKeys() context.keygen.generate_evaluation_keys(seal.dbc_max(), self.ev_keys) def encode_rationals(self, numbers) -> List[Plaintext]: # encoding without encryption encoded_coefficients = [] for i in range(len(numbers)): encoded_coefficients.append(self.encoder.encode(numbers[i])) return encoded_coefficients def encode_num(self, num) -> Plaintext: if type(num) == Plaintext or num is None: return num else: # encoding without encryption return self.encoder.encode(num) def sum_enc_array(self, array: List[Ciphertext]) -> Ciphertext: # can applied for 1D array only encrypted_result = Ciphertext() self.evaluator.add_many(array, encrypted_result) return encrypted_result def encrypt_rationals(self, rational_numbers: List) -> List[Ciphertext]: """ :param rational_numbers: array of rational numbers :return: encrypted result """ encrypted_rationals = [] for i in range(len(rational_numbers)): encrypted_rationals.append(Ciphertext(self.context.params)) self.encryptor.encrypt(self.encoder.encode(rational_numbers[i]), encrypted_rationals[i]) return encrypted_rationals def weighted_average(self, encrypted_rationals, encoded_coefficients, encoded_divide_by) -> Ciphertext: """ Weighted average, where weights encoded, not encrypted numbers :param encoded_divide_by: fixed point fractional, e.g 0.1 to perform division by 10 :return: encrypted result """ for i in range(len(encrypted_rationals)): self.evaluator.multiply_plain(encrypted_rationals[i], encoded_coefficients[i]) encrypted_result = Ciphertext() self.evaluator.add_many(encrypted_rationals, encrypted_result) self.evaluator.multiply_plain(encrypted_result, encoded_divide_by) return encrypted_result def subtract(self, a: Ciphertext, b: Ciphertext) -> Ciphertext: """ Substruction operation of 2 fractional numbers :param a: encrypted fractional value :param b: encrypted fractional value :return: substracted result """ a = deepcopy(a) minus_sign = self.encoder.encode(-1) self.evaluator.multiply_plain(b, minus_sign) self.evaluator.add(a, b) return a def encrypt_num(self, value) -> Ciphertext: if type(value) == Ciphertext or value is None: return value else: encrypted = Ciphertext() if type(value) == Plaintext: self.encryptor.encrypt(value, encrypted) else: plain = self.encoder.encode(value) self.encryptor.encrypt(plain, encrypted) return encrypted def add(self, a: Ciphertext, b: Ciphertext) -> Ciphertext: """ :param a: encrypted fractional value :param b: encrypted fractional value :return: encrypted sum of a and b """ a = deepcopy(a) self.evaluator.add(a, b) return a def add_plain(self, a: Ciphertext, b: Plaintext) -> Ciphertext: """ :param a: encrypted fractional value :param b: encoded fractional value :return: encrypted sum of a and b """ a = deepcopy(a) self.evaluator.add_plain(a, b) return a def multiply(self, a: Ciphertext, b: Ciphertext) -> Ciphertext: """ :param a: encrypted fractional value :param b: encrypted fractional value :return: encrypted product of a and b """ a = deepcopy(a) self.evaluator.multiply(a, b) return a def multiply_plain(self, a: Ciphertext, b: Plaintext) -> Ciphertext: """ :param a: encrypted fractional value :param b: encrypted fractional value :return: encrypted product of a and b """ a = deepcopy(a) self.evaluator.multiply_plain(a, b) return a def relinearize(self, a: Ciphertext) -> Ciphertext: """ :param a: encrypted fractional value, supposedly result of multiplication :return: relinearized a """ a = deepcopy(a) self.evaluator.relinearize(a, self.ev_keys) return a
class CipherMatrix: """ """ def __init__(self, matrix=None): """ :param matrix: numpy.ndarray to be encrypted. """ self.parms = EncryptionParameters() self.parms.set_poly_modulus("1x^2048 + 1") self.parms.set_coeff_modulus(seal.coeff_modulus_128(2048)) self.parms.set_plain_modulus(1 << 8) self.context = SEALContext(self.parms) # self.encoder = IntegerEncoder(self.context.plain_modulus()) self.encoder = FractionalEncoder(self.context.plain_modulus(), self.context.poly_modulus(), 64, 32, 3) self.keygen = KeyGenerator(self.context) self.public_key = self.keygen.public_key() self.secret_key = self.keygen.secret_key() self.encryptor = Encryptor(self.context, self.public_key) self.decryptor = Decryptor(self.context, self.secret_key) self.evaluator = Evaluator(self.context) self._saved = False self._encrypted = False self._id = '{0:04d}'.format(np.random.randint(1000)) if matrix is not None: assert len( matrix.shape) == 2, "Only 2D numpy matrices accepted currently" self.matrix = np.copy(matrix) self.encrypted_matrix = np.empty(self.matrix.shape, dtype=object) for i in range(self.matrix.shape[0]): for j in range(self.matrix.shape[1]): self.encrypted_matrix[i, j] = Ciphertext() else: self.matrix = None self.encrypted_matrix = None print(self._id, "Created") def __repr__(self): """ :return: """ print("Encrypted:", self._encrypted) if not self._encrypted: print(self.matrix) return "" else: return '[]' def __str__(self): """ :return: """ print("| Encryption parameters:") print("| poly_modulus: " + self.context.poly_modulus().to_string()) # Print the size of the true (product) coefficient modulus print("| coeff_modulus_size: " + ( str)(self.context.total_coeff_modulus().significant_bit_count()) + " bits") print("| plain_modulus: " + (str)(self.context.plain_modulus().value())) print("| noise_standard_deviation: " + (str)(self.context.noise_standard_deviation())) if self.matrix is not None: print(self.matrix.shape) return str(type(self)) def __add__(self, other): """ :param other: :return: """ assert isinstance( other, CipherMatrix), "Can only be added with a CipherMatrix" A_enc = self._encrypted B_enc = other._encrypted if A_enc: A = self.encrypted_matrix else: A = self.matrix if B_enc: B = other.encrypted_matrix else: B = other.matrix assert A.shape == B.shape, "Dimension mismatch, Matrices must be of same shape. Got {} and {}".format( A.shape, B.shape) shape = A.shape result = CipherMatrix(np.zeros(shape, dtype=np.int32)) result._update_cryptors(self.get_keygen()) if A_enc: if B_enc: res_mat = result.encrypted_matrix for i in range(shape[0]): for j in range(shape[1]): self.evaluator.add(A[i, j], B[i, j], res_mat[i, j]) result._encrypted = True else: res_mat = result.encrypted_matrix for i in range(shape[0]): for j in range(shape[1]): self.evaluator.add_plain(A[i, j], self.encoder.encode(B[i, j]), res_mat[i, j]) result._encrypted = True else: if B_enc: res_mat = result.encrypted_matrix for i in range(shape[0]): for j in range(shape[1]): self.evaluator.add_plain(B[i, j], self.encoder.encode(A[i, j]), res_mat[i, j]) result._encrypted = True else: result.matrix = A + B result._encrypted = False return result def __sub__(self, other): """ :param other: :return: """ assert isinstance(other, CipherMatrix) if other._encrypted: shape = other.encrypted_matrix.shape for i in range(shape[0]): for j in range(shape[1]): self.evaluator.negate(other.encrypted_matrix[i, j]) else: other.matrix = -1 * other.matrix return self + other def __mul__(self, other): """ :param other: :return: """ assert isinstance( other, CipherMatrix), "Can only be multiplied with a CipherMatrix" # print("LHS", self._id, "RHS", other._id) A_enc = self._encrypted B_enc = other._encrypted if A_enc: A = self.encrypted_matrix else: A = self.matrix if B_enc: B = other.encrypted_matrix else: B = other.matrix Ashape = A.shape Bshape = B.shape assert Ashape[1] == Bshape[0], "Dimensionality mismatch" result_shape = [Ashape[0], Bshape[1]] result = CipherMatrix(np.zeros(shape=result_shape)) if A_enc: if B_enc: for i in range(Ashape[0]): for j in range(Bshape[1]): result_array = [] for k in range(Ashape[1]): res = Ciphertext() self.evaluator.multiply(A[i, k], B[k, j], res) result_array.append(res) self.evaluator.add_many(result_array, result.encrypted_matrix[i, j]) result._encrypted = True else: for i in range(Ashape[0]): for j in range(Bshape[1]): result_array = [] for k in range(Ashape[1]): res = Ciphertext() self.evaluator.multiply_plain( A[i, k], self.encoder.encode(B[k, j]), res) result_array.append(res) self.evaluator.add_many(result_array, result.encrypted_matrix[i, j]) result._encrypted = True else: if B_enc: for i in range(Ashape[0]): for j in range(Bshape[1]): result_array = [] for k in range(Ashape[1]): res = Ciphertext() self.evaluator.multiply_plain( B[i, k], self.encoder.encode(A[k, j]), res) result_array.append(res) self.evaluator.add_many(result_array, result.encrypted_matrix[i, j]) result._encrypted = True else: result.matrix = np.matmul(A, B) result._encrypted = False return result def save(self, path): """ :param path: :return: """ save_dir = os.path.join(path, self._id) if self._saved: print("CipherMatrix already saved") else: assert not os.path.isdir(save_dir), "Directory already exists" os.mkdir(save_dir) if not self._encrypted: self.encrypt() shape = self.encrypted_matrix.shape for i in range(shape[0]): for j in range(shape[1]): element_name = str(i) + '-' + str(j) + '.ahem' self.encrypted_matrix[i, j].save( os.path.join(save_dir, element_name)) self.secret_key.save("/keys/" + "." + self._id + '.wheskey') self._saved = True return save_dir def load(self, path, load_secret_key=False): """ :param path: :param load_secret_key: :return: """ self._id = path.split('/')[-1] print("Loading Matrix:", self._id) file_list = os.listdir(path) index_list = [[file.split('.')[0].split('-'), file] for file in file_list] M = int(max([int(ind[0][0]) for ind in index_list])) + 1 N = int(max([int(ind[0][1]) for ind in index_list])) + 1 del self.encrypted_matrix self.encrypted_matrix = np.empty([M, N], dtype=object) for index in index_list: i = int(index[0][0]) j = int(index[0][1]) self.encrypted_matrix[i, j] = Ciphertext() self.encrypted_matrix[i, j].load(os.path.join(path, index[1])) if load_secret_key: self.secret_key.load("/keys/" + "." + self._id + '.wheskey') self.matrix = np.empty(self.encrypted_matrix.shape) self._encrypted = True def encrypt(self, matrix=None, keygen=None): """ :param matrix: :return: """ assert not self._encrypted, "Matrix already encrypted" if matrix is not None: assert self.matrix is None, "matrix already exists" self.matrix = np.copy(matrix) shape = self.matrix.shape self.encrypted_matrix = np.empty(shape, dtype=object) if keygen is not None: self._update_cryptors(keygen) for i in range(shape[0]): for j in range(shape[1]): val = self.encoder.encode(self.matrix[i, j]) self.encrypted_matrix[i, j] = Ciphertext() self.encryptor.encrypt(val, self.encrypted_matrix[i, j]) self._encrypted = True def decrypt(self, encrypted_matrix=None, keygen=None): """ :return: """ if encrypted_matrix is not None: self.encrypted_matrix = encrypted_matrix assert self._encrypted, "No encrypted matrix" del self.matrix shape = self.encrypted_matrix.shape self.matrix = np.empty(shape) if keygen is not None: self._update_cryptors(keygen) for i in range(shape[0]): for j in range(shape[1]): plain_text = Plaintext() self.decryptor.decrypt(self.encrypted_matrix[i, j], plain_text) self.matrix[i, j] = self.encoder.decode(plain_text) self._encrypted = False return np.copy(self.matrix) def get_keygen(self): """ :return: """ return self.keygen def _update_cryptors(self, keygen): """ :param keygen: :return: """ self.keygen = keygen self.public_key = keygen.public_key() self.secret_key = keygen.secret_key() self.encryptor = Encryptor(self.context, self.public_key) self.decryptor = Decryptor(self.context, self.secret_key) return
def unit_test(): parms = EncryptionParameters() parms.set_poly_modulus("1x^8192 + 1") parms.set_coeff_modulus(seal.coeff_modulus_128(8192)) parms.set_plain_modulus(1 << 10) context = SEALContext(parms) # Print the parameters that we have chosen print_parameters(context) encoder = FractionalEncoder(context.plain_modulus(), context.poly_modulus(), 64, 32, 3) keygen = KeyGenerator(context) public_key = keygen.public_key() secret_key = keygen.secret_key() encryptor = Encryptor(context, public_key) # Computations on the ciphertexts are performed with the Evaluator class. evaluator = Evaluator(context) # We will of course want to decrypt our results to verify that everything worked, # so we need to also construct an instance of Decryptor. Note that the Decryptor # requires the secret key. decryptor = Decryptor(context, secret_key) learningRate = 0.1 learningRate_data = encoder.encode(learningRate) learningRate_e = Ciphertext() encryptor.encrypt(learningRate_data, learningRate_e) updatedVals = [] updatedVals.append(50) updatedVals.append(50) updatedVals_unenc = [] updatedVals_unenc.append(updatedVals[0]) updatedVals_unenc.append(updatedVals[1]) for i in range(15): x = 1 y = 1 updatedVals = learn(x, y, evaluator, updatedVals[0], updatedVals[1], encoder, encryptor, learningRate_e, decryptor) ypred = updatedVals_unenc[0] * x + updatedVals_unenc[1] error = ypred - y updatedVals_unenc[0] = updatedVals_unenc[0] - x * error * learningRate updatedVals_unenc[1] = updatedVals_unenc[1] - error * learningRate print((str)(updatedVals[1]) + ":" + (str)(updatedVals[0]) + ":" + (str)(updatedVals_unenc[1]) + ":" + (str)(updatedVals_unenc[0])) x = 2 y = 3 updatedVals = learn(x, y, evaluator, updatedVals[0], updatedVals[1], encoder, encryptor, learningRate_e, decryptor) ypred = updatedVals_unenc[0] * x + updatedVals_unenc[1] error = ypred - y updatedVals_unenc[0] = updatedVals_unenc[0] - x * error * learningRate updatedVals_unenc[1] = updatedVals_unenc[1] - error * learningRate print((str)(updatedVals[1]) + ":" + (str)(updatedVals[0]) + ":" + (str)(updatedVals_unenc[1]) + ":" + (str)(updatedVals_unenc[0])) x = 4 y = 3 updatedVals = learn(x, y, evaluator, updatedVals[0], updatedVals[1], encoder, encryptor, learningRate_e, decryptor) ypred = updatedVals_unenc[0] * x + updatedVals_unenc[1] error = ypred - y updatedVals_unenc[0] = updatedVals_unenc[0] - x * error * learningRate updatedVals_unenc[1] = updatedVals_unenc[1] - error * learningRate print((str)(updatedVals[1]) + ":" + (str)(updatedVals[0]) + ":" + (str)(updatedVals_unenc[1]) + ":" + (str)(updatedVals_unenc[0])) x = 3 y = 2 updatedVals = learn(x, y, evaluator, updatedVals[0], updatedVals[1], encoder, encryptor, learningRate_e, decryptor) ypred = updatedVals_unenc[0] * x + updatedVals_unenc[1] error = ypred - y updatedVals_unenc[0] = updatedVals_unenc[0] - x * error * learningRate updatedVals_unenc[1] = updatedVals_unenc[1] - error * learningRate print((str)(updatedVals[1]) + ":" + (str)(updatedVals[0]) + ":" + (str)(updatedVals_unenc[1]) + ":" + (str)(updatedVals_unenc[0])) x = 5 y = 5 updatedVals = learn(x, y, evaluator, updatedVals[0], updatedVals[1], encoder, encryptor, learningRate_e, decryptor) ypred = updatedVals_unenc[0] * x + updatedVals_unenc[1] error = ypred - y updatedVals_unenc[0] = updatedVals_unenc[0] - x * error * learningRate updatedVals_unenc[1] = updatedVals_unenc[1] - error * learningRate print((str)(updatedVals[1]) + ":" + (str)(updatedVals[0]) + ":" + (str)(updatedVals_unenc[1]) + ":" + (str)(updatedVals_unenc[0]))
plain_A = [] A = [] A_inv = [] n = int(input("Enter dimension: ")) for i in range(n): plain_a = [] a = [] a_i = [] for j in range(n): encrypted_data1 = Ciphertext() enc_dat = Ciphertext() ran = random.randint(0, 10) plain_a.append(ran) encryptor.encrypt(encoderF.encode(ran), encrypted_data1) encryptor.encrypt(encoderF.encode(0), enc_dat) a.append(encrypted_data1) a_i.append(enc_dat) A.append(a) A_inv.append(a_i) plain_A.append(plain_a) print(plain_a) #tA_=numpy.transpose(A) tA = [list(tup) for tup in zip(*A)] matrixPower_vector = [A] trace_vector = [trace(A)] #count=0
for j in range(8): # add code to combine appended matrix and normal matrix together D=A_cipherObject #shallow copy # reducing to diagnol matrix for i in range(4): for j in range (8): if (j!=i): plain_result = Plaintext() X=D[i][i] decryptor.decrypt(X, plain_result) E=1/int(encoder.decode_int32(plain_result)) Y=Ciphertext(parms) R=encoderF.encode(E) encryptor.encrypt(R,Y) evaluator.multiply(Y,D[j][i]) for k in range(8): evaluator.multiply(Y,D[i][k]) evaluator.negate(Y) evaluator.add(A_cipherObject[j][k],Y) # reducing to unit matrix for i in range (8): d=A_cipherObject[i][i] Y=Ciphertext() plain_result = Plaintext() decryptor.decrypt(X, plain_result) encryptor.encrpyt(encoder.encode(1/int(encoder.decode_int32(plain_result))),Y) for j in range (8):
print("asdk;vknasifnbsdf") normalize(rawX0) # have to find a way to make normalize an encrytped function for i in range(len(rawX0)): rawX0[i] = rawX0[i][1:] tX = [[1] * 245] + rawX0 # encrypting matrix tX tX_encrypted = [] for i in range(n): tx_enc = [] for j in range(m): temp = Ciphertext() encryptor.encrypt(encoderF.encode(S_encoded[i][j]), temp) tx_enc.append(temp) tX_encrypted.append(tx_enc) X = [list(tup) for tup in zip(*tX)] for i in range(len(y)): temp = Ciphertext() encryptor.encrypt(encoderF.encode(int(y[i])), temp) y[i] = temp k = len(X[0]) # k =3 print("here1") U1 = matMultiply(tX_encrypted, y) cross_X = matMultiply(tX_encrypted, X)
public_key = keygen.public_key() secret_key = keygen.secret_key() #ev_keys40 = EvaluationKeys #ev_keys20 = EvaluationKeys() #keygen.generate_evaluation_keys(40,5,ev_keys40) #keygen.generate_evaluation_keys(20,3,ev_keys20) encryptor = Encryptor(context, public_key) evaluator = Evaluator(context) decryptor = Decryptor(context, secret_key) plain_A = [] A=[] n=int(input("Enter dimension: ")) for i in range(n): plain_a = [] a=[] for j in range(n): encrypted_data1= Ciphertext() ran=random.randint(0,10) plain_a.append(ran) encryptor.encrypt(encoderF.encode(ran), encrypted_data1) a.append(encrypted_data1) A.append(a) plain_A.append(plain_a) print(plain_a) delta, C = matrixOperations.inverseMatrix(A) print(delta)
parms.set_coeff_modulus(seal.coeff_modulus_128(8192)) parms.set_plain_modulus(1 << 21) context = SEALContext(parms) encoderF = FractionalEncoder(context.plain_modulus(), context.poly_modulus(), 30, 34, 3) keygen = KeyGenerator(context) public_key = keygen.public_key() secret_key = keygen.secret_key() encryptor = Encryptor(context, public_key) evaluator = Evaluator(context) decryptor = Decryptor(context, secret_key) ########################## encoding main matrix ################################ A = [Ciphertext(), Ciphertext(), Ciphertext(), Ciphertext()] for i in range(len(A)): encryptor.encrypt(encoderF.encode(i), A[i]) for j in range(10): evaluator.multiply(A[0], A[1]) evaluator.multiply(A[0], A[2]) evaluator.add(A[1], A[2]) for i in range(len(A)): print("Noise budget of [" + str(i) + "] :" + str((decryptor.invariant_noise_budget(A[i]))) + " bits") print("A[%d]: " % (i), ) print_value(A[i])