def test_parallel_sequence_running_time(self):
X = np.ones((50, 50))
curr_time1 = time.time()
encryption.encrypt_matrix(self.publickey, X)
curr_time2 = time.time()
seq_running_time = curr_time2 - curr_time1
distribute_encrypt_matrix(self.publickey, X)
curr_time3 = time.time()
parallel_running_time = curr_time3 - curr_time2
assert seq_running_time - parallel_running_time > 0
def send_components(self):
if self.is_min_gen_enc:
self.logger.debug("using min_gen_enc")
self._compute_components()
# phi has shape (1, feature_dim)
# phi_2 has shape (feature_dim, feature_dim)
enc_phi = encryption.encrypt_matrix(self.public_key, self.phi)
enc_phi_2 = distribute_encrypt_matmul_2_ob(self.phi.transpose(),
enc_phi)
# enc_y_overlap_2_phi_2 = 0.25 * np.expand_dims(self.y_overlap_2, axis=2) * enc_phi_2
# enc_y_overlap_phi = -0.5 * self.y_overlap * enc_phi
enc_y_overlap_2_phi_2 = distribute_compute_XY(
0.25 * np.expand_dims(self.y_overlap_2, axis=2),
np.tile(enc_phi_2, (self.y_overlap_2.shape[0], 1, 1)))
enc_y_overlap_phi = distribute_compute_XY(
-0.5 * self.y_overlap,
np.tile(enc_phi, (self.y_overlap.shape[0], 1)))
enc_mapping_comp_A = distribute_encrypt_matrix(
self.public_key, self.mapping_comp_A)
return [
enc_y_overlap_2_phi_2, enc_y_overlap_phi, enc_mapping_comp_A
]
else:
components = super(EncryptedFTLGuestModel, self).send_components()
return self.__encrypt_components(components)
def distribute_encrypt(public_key, X):
"""
encrypt X
:param X: DTable
:return: a dictionary
"""
X2 = X.mapValues(lambda x: encrypt_matrix(public_key, x))
val = X2.collect()
val = dict(val)
return val