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])
