def decrypt(self, raw_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) # Unpickle, base64 decode, and decrypt each ciphertext result decrypted_data = [] for d in raw_data: encrypted_data = pickle.loads( base64.b64decode(d) ) plain_data = Plaintext() self.decryptor.decrypt(encrypted_data, plain_data) decrypted_data.append( str(encoder.decode(plain_data)) ) return decrypted_data
class FractionalDecryptorUtils: def __init__(self, context): """ Class providing decryption operation :param context: Context initialising HE parameters """ self.context = context.context self.secret_key = context.secret_key self.decryptor = Decryptor(self.context, self.secret_key) self.encoder = FractionalEncoder(self.context.plain_modulus(), self.context.poly_modulus(), 64, 32, 3) self.evaluator = context.evaluator def decrypt(self, encrypted_res): plain_result = Plaintext() self.decryptor.decrypt(encrypted_res, plain_result) result = self.encoder.decode(plain_result) return result
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 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 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
for i in range(len(matrixPower_vector) - 1): for j in range(len(c)): if (i + j == n - 1): mult(c[j], matrixPower_vector[i]) for t in range(n): for s in range(n): evaluator.add(A_inv[t][s], matrixPower_vector[i][t][s]) # decrypted inverse matrix A_i_dec = [] for x in A_inv: a_i = [] for y in x: p = Plaintext() decryptor.decrypt(y, p) a_i.append(encoderF.decode(p)) A_i_dec.append(a_i) decryptor.decrypt(c[n], p) # nth coefficient of characteristic equation of th determin = encoderF.decode(p) print("negative of co-factor matrix: ", A_i_dec) A_i_dec = [[(-1 / determin) * elem for elem in row] for row in A_i_dec] print("\nThe inverse matrix:\n", np.asarray(A_i_dec)) print('Time cost: {} seconds'.format(time.time() - t1)) t2 = time.time() np_A_inv = np.linalg.inv(np.asarray(plain_A)) print('Inverse computed by numpy: \n{}'.format(np_A_inv)) print('Time cost: {} second'.format(time.time() - t2))