def test_add_diff():
secret_key = b'1' * 48
encryptor1 = generate_onetime_pad_encryptor(secret_key)
encryptor2 = generate_onetime_pad_encryptor(secret_key)
x = np.array([random.random()], dtype=np.float32)
y = np.array([random.random()], dtype=np.float32)
enc_x = encryptor1.encrypt(x, alpha=-1)
enc_y = encryptor2.encrypt(y, alpha=2)
assert almost_equal(encryptor1.decrypt(enc_x + enc_y, alpha=1), x + y)
x = np.random.uniform(-1, 1, (512, )).astype(np.float32)
y = np.random.uniform(-1, 1, (512, )).astype(np.float32)
enc_x = encryptor1.encrypt(x, alpha=-1)
enc_y = encryptor2.encrypt(y, alpha=2)
assert almost_equal(encryptor1.decrypt(enc_x + enc_y, alpha=1), (x + y))
x = torch.Tensor(512, ).uniform_(-1, 1)
y = torch.Tensor(512, ).uniform_(-1, 1)
enc_x = encryptor1.encrypt(x, alpha=-1)
enc_y = encryptor2.encrypt(y, alpha=2)
assert almost_equal(encryptor1.decrypt(enc_x + enc_y, alpha=1), (x + y))
x = [[np.random.uniform(-1, 1, (512, )).astype(np.float32)],
[torch.Tensor(512, ).uniform_(-1, 1)]]
y = [[np.random.uniform(-1, 1, (512, )).astype(np.float32)],
[torch.Tensor(512, ).uniform_(-1, 1)]]
enc_x = encryptor1.encrypt(x, alpha=-1)
enc_y = encryptor2.encrypt(y, alpha=2)
assert almost_equal(encryptor1.decrypt(enc_x + enc_y, alpha=1),
iterative_add(x, y))
def test_mul():
x1 = random.random()
x2 = random.random()
y1 = np.random.random(100).astype(np.float32)
y2 = np.random.random(100).astype(np.float32)
enc_x1 = pe.encrypt(x1)
enc_y1 = pe.encrypt(y1)
assert almost_equal(pd.decrypt(enc_x1 * x2), x1 * x2) # float * float
assert almost_equal(pd.decrypt(enc_y1 * x1), y1 * x1) # float * array
assert almost_equal(pd.decrypt(enc_y1 * y2), y1 * y2) # array * array
def test_add_mul_numpy_parallel():
x = np.random.random(100).astype(np.float32)
y = np.random.random(100).astype(np.float32)
en_x = pe.encrypt(x)
en_y = pe.encrypt(y)
en_result = parallel_ops.add(en_x, y)
result = pd.decrypt(en_result)
assert almost_equal(x + y, result)
en_result = parallel_ops.add(en_x, en_y)
result = pd.decrypt(en_result)
assert almost_equal(x + y, result)
en_result = parallel_ops.mul(en_x, y)
result = pd.decrypt(en_result)
assert almost_equal(x * y, result)
def test_encrypt_decrypt():
secret_key = b'1' * 48
encryptor = generate_onetime_pad_encryptor(secret_key)
x = np.random.uniform(-1, 1, (512, )).astype(np.float32)
en_x = encryptor.encrypt(x, alpha=1)
z = encryptor.decrypt(en_x, alpha=1)
assert almost_equal(z, x)
x = torch.Tensor(512, ).uniform_(-1, 1)
en_x = encryptor.encrypt(x, alpha=1)
z = encryptor.decrypt(en_x, alpha=1)
assert almost_equal(z, x)
x = [[np.random.uniform(-1, 1, (512, )).astype(np.float32)],
[torch.Tensor(512, ).uniform_(-1, 1)]]
en_x = encryptor.encrypt(x, alpha=1)
z = encryptor.decrypt(en_x, alpha=1)
assert almost_equal(z, x)
def test_encrypt_decrypt():
# float
plain_param1 = random.random()
encrypted_param1 = pe.encrypt(plain_param1)
plain_param2 = pd.decrypt(encrypted_param1)
assert almost_equal(plain_param1, plain_param2)
# one dim array
plain_param1 = np.random.random(100).astype(np.float32)
encrypted_param1 = pe.encrypt(plain_param1)
plain_param2 = pd.decrypt(encrypted_param1)
assert almost_equal(plain_param1, plain_param2)
# two dim array
plain_param1 = np.random.random((128, 4)).astype(np.float32)
encrypted_param1 = pe.encrypt(plain_param1)
plain_param2 = pd.decrypt(encrypted_param1)
assert almost_equal(plain_param1, plain_param2)
def test_he_otp_lr_ft1():
federal_info = fed_conf_guest
sec_param = {"he_algo": 'paillier', "he_key_length": 1024}
prng = RandomState(0)
guest_theta = prng.uniform(-1, 1, (6, ))
guest_features = prng.uniform(-1, 1, (32, 6))
guest_labels = prng.randint(0, 2, (32, ))
host_theta = prng.uniform(-1, 1, (6, ))
host_features = prng.uniform(-1, 1, (32, 6))
def calu_grad(guest_theta, guest_features, guest_labels, host_theta,
host_features):
u2 = host_theta.dot(host_features.T)
u1 = guest_theta.dot(guest_features.T)
u = u1 + u2
h_x = 1 / (1 + np.exp(-u))
batch_size = guest_features.shape[0]
grads = (-1 / batch_size) * ((guest_labels - h_x).dot(guest_features))
return h_x, grads
# print(guest_theta, guest_features, guest_labels)
trainer = make_protocol(HE_OTP_LR_FT1,
federal_info,
sec_param,
algo_param=None)
# 联邦计算结果
fed_h_x, fed_grads = trainer.exchange(guest_theta, guest_features,
guest_labels)
# 本地计算结果
local_h_x, local_grads = calu_grad(guest_theta, guest_features,
guest_labels, host_theta, host_features)
assert almost_equal(fed_h_x, local_h_x)
assert almost_equal(fed_grads, local_grads)
def test_add_decode():
secret_key = b'1' * 48
encryptor1 = generate_onetime_pad_encryptor(secret_key)
encryptor2 = generate_onetime_pad_encryptor(secret_key)
x = [[np.random.uniform(-1, 1, (512, )).astype(np.float32)],
[torch.Tensor(512, ).uniform_(-1, 1)]]
y = [[np.random.uniform(-1, 1, (512, )).astype(np.float32)],
[torch.Tensor(512, ).uniform_(-1, 1)]]
enc_x = encryptor1.encrypt(x, alpha=-1)
enc_y = encryptor2.encrypt(y, alpha=1)
assert almost_equal((enc_x + enc_y).decode(), iterative_add(x, y))
def test_he_linear_ft():
# host和guest的随机初始状态相同
prng = RandomState(0)
guest_u = np.array(prng.uniform(-1, 1, (8, )))
host_u = np.array(prng.uniform(-1, 1, (8, )))
guest_labels = np.array(prng.randint(0, 2, (8, )))
federal_info = fed_conf_host
sec_param = {"he_algo": 'paillier', "he_key_length": 1024}
trainer = make_protocol(HE_LINEAR_FT,
federal_info,
sec_param,
algo_param=None)
result = trainer.exchange(host_u)
assert almost_equal(result, guest_u + host_u - guest_labels)
def test_predict():
u1 = [
np.array([0.1, 0.05, -3.6, 25.8], dtype=np.float32),
np.array([-0.5, 11.2, 9.5], dtype=np.float32)
]
u2 = [
np.array([0.3, -14, -2.5, 1.7], dtype=np.float32),
np.array([0.2, 1.2, -5.6], dtype=np.float32)
]
expected_u = [u1[i] + u2[i] for i in range(len(u1))]
federal_info = fed_conf_guest
sec_param = {"he_algo": 'paillier', "he_key_length": 1024}
protocol = make_protocol(HE_LR_FP, federal_info, sec_param, None)
for i in range(len(u1)):
u = protocol.exchange(u1[i])
assert almost_equal(u, expected_u[i])
def test():
theta = [[
np.random.uniform(-1, 1, (2, 4)).astype(np.float32),
np.random.uniform(-1, 1, (2, 6)).astype(np.float32)
], [np.random.uniform(-1, 1, (2, 8)).astype(np.float32)]]
print(theta)
federal_info = fed_conf_host
sec_param = {"key_exchange_size": 2048}
trainer = make_protocol(OTP_SA_FT, federal_info, sec_param)
result = trainer.exchange(theta)
var_chan = make_variable_channel('test_otp_sa_ft',
fed_conf_host["federation"]["host"][0],
fed_conf_host["federation"]["guest"][0])
guest_theta = var_chan.recv(tag='theta')
sum_theta = iterative_add(theta, guest_theta)
# 本地计算的平均梯度
avg_theta = iterative_divide(sum_theta, 2.0)
assert almost_equal(result, avg_theta)
def test_add():
x1 = random.random()
x2 = random.random()
y1 = np.random.random(100).astype(np.float32)
y2 = np.random.random(100).astype(np.float32)
z1 = np.random.random((128, 4)).astype(np.float32)
z2 = np.random.random((128, 4)).astype(np.float32)
enc_x1 = pe.encrypt(x1)
enc_x2 = pe.encrypt(x2)
enc_y1 = pe.encrypt(y1)
enc_y2 = pe.encrypt(y2)
enc_z1 = pe.encrypt(z1)
enc_z2 = pe.encrypt(z2)
assert almost_equal(pd.decrypt(enc_x1 + enc_x2), x1 + x2)
assert almost_equal(pd.decrypt(enc_y1 + enc_y2), y1 + y2)
assert almost_equal(pd.decrypt(enc_z1 + enc_z2), z1 + z2)
assert almost_equal(pd.decrypt(enc_x1 + x2), x1 + x2)
assert almost_equal(pd.decrypt(enc_y1 + y2), y1 + y2)
assert almost_equal(pd.decrypt(enc_z1 + z2), z1 + z2)
