def test_generate_and_transfer_primitive(
get_clients: Callable,
nr_parties: int,
nr_instances: int,
nr_instances_retrieve: int,
) -> None:
parties = get_clients(nr_parties)
session = Session(parties=parties)
SessionManager.setup_mpc(session)
g_kwargs = {"nr_parties": nr_parties, "nr_instances": nr_instances}
CryptoPrimitiveProvider.generate_primitives(
"test",
session=session,
g_kwargs=g_kwargs,
p_kwargs={},
)
for i in range(nr_parties):
remote_crypto_store = session.session_ptrs[i].crypto_store
primitives = remote_crypto_store.get_primitives_from_store(
op_str="test", nr_instances=nr_instances_retrieve).get()
assert primitives == [
tuple(ShareTensor(i) for _ in range(PRIMITIVE_NR_ELEMS))
for _ in range(nr_instances_retrieve)
]
def test_rst_invalid_triple(get_clients) -> None:
parties = get_clients(3)
falcon = Falcon("malicious")
session = Session(parties, protocol=falcon)
SessionManager.setup_mpc(session)
shape_x = (1, )
shape_y = (1, )
# create an inconsistent sharing,invoke a prrs first
session.session_ptrs[0].prrs_generate_random_share(shape_x)
with pytest.raises(ValueError):
CryptoPrimitiveProvider.generate_primitives(
"beaver_mul",
session=session,
g_kwargs={
"session": session,
"a_shape": shape_x,
"b_shape": shape_y,
"nr_parties": session.nr_parties,
},
p_kwargs={
"a_shape": shape_x,
"b_shape": shape_y
},
)
def test_transfer_primitives_mismatch_len_exception() -> None:
with pytest.raises(ValueError):
"""Primitives and Sesssions should have the same len."""
CryptoPrimitiveProvider._transfer_primitives_to_parties(op_str="test",
primitives=[1],
sessions=[],
p_kwargs={})
def test_transfer_primitives_type_exception() -> None:
with pytest.raises(ValueError):
"""Primitives should be a list."""
CryptoPrimitiveProvider._transfer_primitives_to_parties(op_str="test",
primitives=50,
sessions=[],
p_kwargs={})
def test_generate_primitive_from_dict_beaver_mul(get_clients) -> None:
clients = get_clients(2)
session = Session(parties=clients)
SessionManager.setup_mpc(session)
primitive_log = {
"beaver_mul": [
(
{
"a_shape": (2, 10),
"b_shape": (2, 10)
},
{
"session": session,
"a_shape": (2, 10),
"b_shape": (2, 10),
"nr_parties": 2,
},
),
(
{
"a_shape": (1, 5),
"b_shape": (1, 5)
},
{
"session": session,
"a_shape": (1, 5),
"b_shape": (1, 5),
"nr_parties": 2,
},
),
]
}
CryptoPrimitiveProvider.generate_primitive_from_dict(
primitive_log=primitive_log, session=session)
args = primitive_log.popitem()[1]
for arg in args:
a_shape = arg[1].get("a_shape")
b_shape = arg[1].get("b_shape")
key = f"beaver_mul_{a_shape}_{b_shape}"
store_client_1 = session.session_ptrs[0].crypto_store.store.get()
store_client_2 = session.session_ptrs[1].crypto_store.store.get()
a_shape_client_1 = tuple(store_client_1.get(key)[0][0].shape)
b_shape_client_1 = tuple(store_client_1.get(key)[0][1].shape)
assert a_shape == a_shape_client_1
assert b_shape == b_shape_client_1
a_shape_client_2 = tuple(store_client_2.get(key)[0][0].shape)
b_shape_client_2 = tuple(store_client_2.get(key)[0][1].shape)
assert a_shape == a_shape_client_2
assert b_shape == b_shape_client_2
def test_primitive_logging_model(get_clients) -> None:
model = LinearNet(torch)
clients = get_clients(2)
session = Session(parties=clients)
SessionManager.setup_mpc(session)
mpc_model = model.share(session=session)
x_secret = torch.randn(2, 3)
x_mpc = MPCTensor(secret=x_secret, session=session)
model.eval()
expected_primitive_log = {
"beaver_matmul": [(
{
"a_shape": (2, 3),
"b_shape": (3, 10)
},
{
"session": session,
"a_shape": (2, 3),
"b_shape": (3, 10),
"nr_parties": 2,
},
)],
"fss_comp": [({}, {
"n_values": 20
})],
"beaver_mul": [(
{
"a_shape": (2, 10),
"b_shape": (2, 10)
},
{
"session": session,
"a_shape": (2, 10),
"b_shape": (2, 10),
"nr_parties": 2,
},
)],
}
CryptoPrimitiveProvider.start_logging()
mpc_model(x_mpc)
primitive_log = CryptoPrimitiveProvider.stop_logging()
assert expected_primitive_log == primitive_log
def fss_op(x1: MPCTensor, x2: MPCTensor, op="eq") -> MPCTensor:
"""Define the workflow for a binary operation using Function Secret Sharing.
Currently supported operand are = & <=, respectively corresponding to
op = 'eq' and 'comp'.
Args:
x1 (MPCTensor): First private value.
x2 (MPCTensor): Second private value.
op: Type of operation to perform, should be 'eq' or 'comp'. Defaults to eq.
Returns:
MPCTensor: Shares of the comparison.
"""
assert not th.cuda.is_available() # nosec
# FIXME: Better handle the case where x1 or x2 is not a MPCTensor. For the moment
# FIXME: we cast it into a MPCTensor at the expense of extra communication
session = x1.session
dtype = session.tensor_type
shape = MPCTensor._get_shape("sub", x1.shape, x2.shape)
n_values = shape.numel()
CryptoPrimitiveProvider.generate_primitives(
f"fss_{op}",
sessions=session.session_ptrs,
g_kwargs={"n_values": n_values},
p_kwargs={},
)
args = zip(session.session_ptrs, x1.share_ptrs, x2.share_ptrs)
args = [list(el) + [op] for el in args]
shares = parallel_execution(mask_builder, session.parties)(args)
# TODO: don't do .reconstruct(), this should be done remotely between the evaluators
mask_value = MPCTensor(shares=shares, session=session)
mask_value = mask_value.reconstruct(decode=False) % 2**n
# TODO: add dtype to args
args = [(session.session_ptrs[i], th.IntTensor([i]), mask_value, op)
for i in range(2)]
shares = parallel_execution(evaluate, session.parties)(args)
response = MPCTensor(session=session, shares=shares, shape=shape)
response.shape = shape
return response
def test_primitive_logging_beaver_matmul(get_clients) -> None:
clients = get_clients(2)
session = Session(parties=clients)
SessionManager.setup_mpc(session)
p_kwargs = {"a_shape": (2, 3), "b_shape": (3, 10)}
g_kwargs = {"a_shape": (2, 3), "b_shape": (3, 10), "nr_parties": 2}
CryptoPrimitiveProvider.start_logging()
CryptoPrimitiveProvider.generate_primitives(
session=session,
op_str="beaver_matmul",
p_kwargs=p_kwargs,
g_kwargs=g_kwargs,
)
primitive_log = CryptoPrimitiveProvider.stop_logging()
expected_log = {"beaver_matmul": [(p_kwargs, g_kwargs)]}
assert expected_log == primitive_log
def test_generate_primitive_from_dict_beaver_conv2d(get_clients) -> None:
clients = get_clients(2)
session = Session(parties=clients)
SessionManager.setup_mpc(session)
primitive_log = {
"beaver_conv2d": [(
{
"a_shape": (1, 1, 28, 28),
"b_shape": (5, 1, 5, 5)
},
{
"session": session,
"a_shape": (1, 1, 28, 28),
"b_shape": (5, 1, 5, 5),
"nr_parties": 2,
},
)]
}
CryptoPrimitiveProvider.generate_primitive_from_dict(
primitive_log=primitive_log, session=session)
a_shape = (1, 1, 28, 28)
b_shape = (5, 1, 5, 5)
key = f"beaver_conv2d_{a_shape}_{b_shape}"
store_client_1 = session.session_ptrs[0].crypto_store.store.get()
store_client_2 = session.session_ptrs[1].crypto_store.store.get()
a_shape_client_1 = tuple(store_client_1.get(key)[0][0].shape)
b_shape_client_1 = tuple(store_client_1.get(key)[0][1].shape)
assert a_shape == a_shape_client_1
assert b_shape == b_shape_client_1
a_shape_client_2 = tuple(store_client_2.get(key)[0][0].shape)
b_shape_client_2 = tuple(store_client_2.get(key)[0][1].shape)
assert a_shape == a_shape_client_2
assert b_shape == b_shape_client_2
def test_generate_primitive_from_dict_beaver_matmul(get_clients) -> None:
clients = get_clients(2)
session = Session(parties=clients)
SessionManager.setup_mpc(session)
primitive_log = {
"beaver_matmul": [(
{
"a_shape": (2, 3),
"b_shape": (3, 10)
},
{
"a_shape": (2, 3),
"b_shape": (3, 10),
"nr_parties": 2
},
)]
}
CryptoPrimitiveProvider.generate_primitive_from_dict(
primitive_log=primitive_log, session=session)
a_shape = (2, 3)
b_shape = (3, 10)
key = f"beaver_matmul_{a_shape}_{b_shape}"
store_client_1 = session.session_ptrs[0].crypto_store.store.get()
store_client_2 = session.session_ptrs[1].crypto_store.store.get()
a_shape_client_1 = tuple(store_client_1.get(key)[0][0].shape)
b_shape_client_1 = tuple(store_client_1.get(key)[0][1].shape)
assert a_shape == a_shape_client_1
assert b_shape == b_shape_client_1
a_shape_client_2 = tuple(store_client_2.get(key)[0][0].shape)
b_shape_client_2 = tuple(store_client_2.get(key)[0][1].shape)
assert a_shape == a_shape_client_2
assert b_shape == b_shape_client_2
def mul_master(x: MPCTensor, y: MPCTensor, op_str: str) -> MPCTensor:
"""Function that is executed by the orchestrator to multiply two secret values
:return: a new set of shares that represents the multiplication
between two secret values
:rtype: MPCTensor
"""
if op_str not in EXPECTED_OPS:
raise ValueError(f"{op_str} should be in {EXPECTED_OPS}")
session = x.session
shape_x = tuple(x.shape)
shape_y = tuple(y.shape)
primitives = CryptoPrimitiveProvider.generate_primitives(
f"beaver_{op_str}",
sessions=session.session_ptrs,
g_kwargs={
"a_shape": shape_x,
"b_shape": shape_y,
"nr_parties": session.nr_parties,
},
p_kwargs={
"a_shape": shape_x,
"b_shape": shape_y
},
)
a_sh, b_sh, c_sh = primitives[0]
a_mpc = MPCTensor(shares=a_sh, shape=x.shape, session=session)
b_mpc = MPCTensor(shares=b_sh, shape=y.shape, session=session)
eps = x - a_mpc
delta = y - b_mpc
eps_plaintext = eps.reconstruct(decode=False)
delta_plaintext = delta.reconstruct(decode=False)
# Arguments that must be sent to all parties
common_args = [eps_plaintext, delta_plaintext, op_str]
# Specific arguments to each party
args = [[el] + common_args for el in session.session_ptrs]
shares = parallel_execution(mul_parties, session.parties)(args)
result = MPCTensor(shares=shares, shape=c_sh[0].shape, session=session)
return result
def test_primitive_logging_beaver_conv2d(get_clients) -> None:
clients = get_clients(2)
session = Session(parties=clients)
SessionManager.setup_mpc(session)
p_kwargs = {"a_shape": (1, 1, 28, 28), "b_shape": (5, 1, 5, 5)}
g_kwargs = {
"a_shape": (1, 1, 28, 28),
"b_shape": (5, 1, 5, 5),
"nr_parties": 2
}
CryptoPrimitiveProvider.start_logging()
CryptoPrimitiveProvider.generate_primitives(
sessions=session.session_ptrs,
op_str="beaver_conv2d",
p_kwargs=p_kwargs,
g_kwargs=g_kwargs,
)
primitive_log = CryptoPrimitiveProvider.stop_logging()
expected_log = {"beaver_conv2d": [(p_kwargs, g_kwargs)]}
assert expected_log == primitive_log
def public_divide(x: MPCTensor, y: Union[torch.Tensor, int]) -> MPCTensor:
"""Function that is executed by the orchestrator to divide a secret by a public value.
Args:
x (MPCTensor): Private numerator.
y (Union[torch.Tensor, int]): Public denominator.
Returns:
MPCTensor: A new set of shares that represents the division.
"""
session = x.session
res_shape = x.shape
if session.nr_parties == 2:
shares = [operator.truediv(share, y) for share in x.share_ptrs]
return MPCTensor(shares=shares, session=session, shape=res_shape)
primitives = CryptoPrimitiveProvider.generate_primitives(
"beaver_wraps",
session=session,
g_kwargs={
"nr_parties": session.nr_parties,
"shape": res_shape
},
p_kwargs=None,
)
r_sh, theta_r_sh = list(zip(*list(zip(*primitives))[0]))
r_mpc = MPCTensor(shares=r_sh, session=session, shape=x.shape)
z = r_mpc + x
z_shares_local = z.get_shares()
common_args = [z_shares_local, y]
args = zip(
r_mpc.share_ptrs,
theta_r_sh,
x.share_ptrs,
)
args = [list(el) + common_args for el in args]
theta_x = parallel_execution(div_wraps, session.parties)(args)
theta_x_plaintext = MPCTensor(shares=theta_x,
session=session).reconstruct()
res = x - theta_x_plaintext * 4 * ((session.ring_size // 4) // y)
return res
def public_divide(x: MPCTensor, y: Union[torch.Tensor, int]) -> MPCTensor:
"""Function that is executed by the orchestrator to divide a secret by a value
(that value is public)
:return: a new set of shares that represents the multiplication
between two secret values
:rtype: MPCTensor
"""
session = x.session
res_shape = x.shape
if session.nr_parties == 2:
shares = [operator.truediv(share, y) for share in x.share_ptrs]
return MPCTensor(shares=shares, session=session, shape=res_shape)
primitives = CryptoPrimitiveProvider.generate_primitives(
"beaver_wraps",
sessions=session.session_ptrs,
g_kwargs={
"nr_parties": session.nr_parties,
"shape": res_shape
},
p_kwargs=None,
)
r_sh, theta_r_sh = primitives[0]
r_mpc = MPCTensor(shares=r_sh, session=session, shape=x.shape)
z = r_mpc + x
z_shares_local = z.get_shares()
common_args = [z_shares_local, y]
args = zip(session.session_ptrs, r_mpc.share_ptrs, theta_r_sh,
x.share_ptrs)
args = [list(el) + common_args for el in args]
theta_x = parallel_execution(div_wraps, session.parties)(args)
theta_x_plaintext = MPCTensor(shares=theta_x,
session=session).reconstruct()
res = x - theta_x_plaintext * 4 * ((session.ring_size // 4) // y)
return res
def test_generate_primitive(get_clients: Callable, nr_parties: int,
nr_instances: int) -> None:
parties = get_clients(nr_parties)
session = Session(parties=parties)
SessionManager.setup_mpc(session)
g_kwargs = {"nr_parties": nr_parties, "nr_instances": nr_instances}
res = CryptoPrimitiveProvider.generate_primitives(
"test",
sessions=session.session_ptrs,
g_kwargs=g_kwargs,
p_kwargs=None,
)
assert isinstance(res, list)
assert len(res) == nr_parties
for i, primitives in enumerate(res):
for primitive in primitives:
assert primitive == tuple(i for _ in range(PRIMITIVE_NR_ELEMS))
def test_exception_mul_malicious(get_clients):
parties = get_clients(3)
protocol = Falcon("malicious")
session = Session(protocol=protocol, parties=parties)
SessionManager.setup_mpc(session)
x = MPCTensor(secret=1, session=session)
y = MPCTensor(secret=2, session=session)
shape_x = tuple(x.shape)
shape_y = tuple(y.shape)
p_kwargs = {"a_shape": shape_x, "b_shape": shape_y}
tensor = torch.tensor([1])
primitives = CryptoPrimitiveProvider.generate_primitives(
"beaver_mul",
session=session,
g_kwargs={
"session": session,
"a_shape": shape_x,
"b_shape": shape_y,
"nr_parties": session.nr_parties,
},
p_kwargs=p_kwargs,
)
party = [0, 2] # modify the primitives of party 0,2
sess_list = [session.session_ptrs[i].get_copy() for i in party]
for i, p in enumerate(party):
idx = 0 if p == 0 else 1
primitives[p][0][0].shares[idx] = tensor
sess_list[i].crypto_store.store = {}
sess_list[i].crypto_store.populate_store("beaver_mul", primitives[p],
**p_kwargs)
session.session_ptrs[p] = sess_list[i].send(parties[p])
with pytest.raises(ValueError):
x * y
def test_exception_init() -> None:
with pytest.raises(ValueError):
CryptoPrimitiveProvider()
def test_generate_primitive_exception() -> None:
with pytest.raises(ValueError):
CryptoPrimitiveProvider.generate_primitives(op_str="SyMPC",
session=Session())
def mul_malicious(
x: MPCTensor,
y: MPCTensor,
session: Session,
op_str: str,
ring_size: int,
config: Config,
**kwargs_: Dict[Any, Any],
) -> MPCTensor:
"""Falcon malicious multiplication.
Args:
x (MPCTensor): Secret
y (MPCTensor): Another secret
session (Session): Session the tensors belong to
op_str (str): Operation string.
ring_size (int) : Ring size of the underlying tensor.
config (Config): The configuration(base,precision) of the underlying tensor.
kwargs_ (Dict[Any, Any]): Kwargs for some operations like conv2d
Returns:
result(MPCTensor): Result of the operation.
Raises:
ValueError : If the shares are not valid.
"""
shape_x = tuple(x.shape)
shape_y = tuple(y.shape)
result = Falcon.mul_semi_honest(x,
y,
session,
op_str,
ring_size,
config,
reshare=True,
**kwargs_)
args = [list(sh) + [op_str] for sh in zip(x.share_ptrs, y.share_ptrs)]
try:
mask = parallel_execution(Falcon.falcon_mask,
session.parties)(args)
except EmptyPrimitiveStore:
CryptoPrimitiveProvider.generate_primitives(
f"beaver_{op_str}",
session=session,
g_kwargs={
"session": session,
"a_shape": shape_x,
"b_shape": shape_y,
"nr_parties": session.nr_parties,
"ring_size": ring_size,
"config": config,
**kwargs_,
},
p_kwargs={
"a_shape": shape_x,
"b_shape": shape_y
},
)
mask = parallel_execution(Falcon.falcon_mask,
session.parties)(args)
# zip on pointers is compute intensive
mask_local = [mask[idx].get() for idx in range(session.nr_parties)]
eps_shares, delta_shares = zip(*mask_local)
eps_plaintext = ReplicatedSharedTensor.reconstruct(eps_shares)
delta_plaintext = ReplicatedSharedTensor.reconstruct(delta_shares)
args = [
list(sh) + [eps_plaintext, delta_plaintext, op_str]
for sh in zip(result.share_ptrs)
]
triple_shares = parallel_execution(Falcon.triple_verification,
session.parties)(args, kwargs_)
triple = MPCTensor(shares=triple_shares, session=x.session)
if (triple.reconstruct(decode=False) == 0).all():
return result
else:
raise ValueError("Computation Aborted: Malicious behavior.")
def mul_master(x: MPCTensor, y: MPCTensor, op_str: str,
kwargs_: Dict[Any, Any]) -> MPCTensor:
"""Function that is executed by the orchestrator to multiply two secret values.
Args:
x (MPCTensor): First value to multiply with.
y (MPCTensor): Second value to multiply with.
op_str (str): Operation string.
kwargs_ (dict): TODO:Add docstring.
Raises:
ValueError: If op_str not in EXPECTED_OPS.
Returns:
MPCTensor: Result of the multiplication.
"""
if op_str not in EXPECTED_OPS:
raise ValueError(f"{op_str} should be in {EXPECTED_OPS}")
session = x.session
shape_x = tuple(x.shape)
shape_y = tuple(y.shape)
CryptoPrimitiveProvider.generate_primitives(
f"beaver_{op_str}",
sessions=session.session_ptrs,
g_kwargs={
"a_shape": shape_x,
"b_shape": shape_y,
"nr_parties": session.nr_parties,
**kwargs_,
},
p_kwargs={
"a_shape": shape_x,
"b_shape": shape_y
},
)
args = [
list(el) + [op_str]
for el in zip(session.session_ptrs, x.share_ptrs, y.share_ptrs)
]
mask = parallel_execution(spdz_mask, session.parties)(args)
eps_shares, delta_shares = zip(*mask)
eps = MPCTensor(shares=eps_shares, session=session)
delta = MPCTensor(shares=delta_shares, session=session)
eps_plaintext = eps.reconstruct(decode=False)
delta_plaintext = delta.reconstruct(decode=False)
# Arguments that must be sent to all parties
common_args = [eps_plaintext, delta_plaintext, op_str]
# Specific arguments to each party
args = [[el] + common_args for el in session.session_ptrs]
shares = parallel_execution(mul_parties, session.parties)(args, kwargs_)
result = MPCTensor(shares=shares, session=session)
return result
def fss_op(x1: MPCTensor, x2: MPCTensor, op="eq") -> MPCTensor:
"""Define the workflow for a binary operation using Function Secret Sharing.
Currently supported operand are = & <=, respectively corresponding to
op = 'eq' and 'comp'.
Args:
x1 (MPCTensor): First private value.
x2 (MPCTensor): Second private value.
op: Type of operation to perform, should be 'eq' or 'comp'. Defaults to eq.
Returns:
MPCTensor: Shares of the comparison.
"""
if th.cuda.is_available():
# FSS is currently not supported on GPU.
# https://stackoverflow.com/a/62145307/8878627
# When the CUDA_VISIBLE_DEVICES environment variable is not set,
# CUDA is not used even if available. Hence, we default to None
cuda_visible_devices = os.environ.get("CUDA_VISIBLE_DEVICES", None)
os.environ["CUDA_VISIBLE_DEVICES"] = ""
warnings.warn("Temporarily disabling CUDA as FSS does not support it")
else:
cuda_visible_devices = None
# FIXME: Better handle the case where x1 or x2 is not a MPCTensor. For the moment
# FIXME: we cast it into a MPCTensor at the expense of extra communication
session = x1.session
shape = MPCTensor._get_shape("sub", x1.shape, x2.shape)
n_values = shape.numel()
CryptoPrimitiveProvider.generate_primitives(
f"fss_{op}",
sessions=session.session_ptrs,
g_kwargs={"n_values": n_values},
p_kwargs={},
)
args = zip(session.session_ptrs, x1.share_ptrs, x2.share_ptrs)
args = [list(el) + [op] for el in args]
shares = parallel_execution(mask_builder, session.parties)(args)
# TODO: don't do .reconstruct(), this should be done remotely between the evaluators
mask_value = MPCTensor(shares=shares, session=session)
mask_value = mask_value.reconstruct(decode=False) % 2**n
# TODO: add dtype to args
args = [(session.session_ptrs[i], th.IntTensor([i]), mask_value, op)
for i in range(2)]
shares = parallel_execution(evaluate, session.parties)(args)
response = MPCTensor(session=session, shares=shares, shape=shape)
response.shape = shape
if cuda_visible_devices is not None:
os.environ["CUDA_VISIBLE_DEVICES"] = cuda_visible_devices
return response
def test_register_primitive() -> None:
val = CryptoPrimitiveProvider.get_state()
expected_providers = "test"
assert expected_providers in val, "Test Provider not registered"
def mul_master(x: MPCTensor, y: MPCTensor, op_str: str,
kwargs_: Dict[Any, Any]) -> MPCTensor:
"""Function that is executed by the orchestrator to multiply two secret values.
Args:
x (MPCTensor): First value to multiply with.
y (MPCTensor): Second value to multiply with.
op_str (str): Operation string.
kwargs_ (dict): TODO:Add docstring.
Raises:
ValueError: If op_str not in EXPECTED_OPS.
Returns:
MPCTensor: Result of the multiplication.
"""
if op_str not in EXPECTED_OPS:
raise ValueError(f"{op_str} should be in {EXPECTED_OPS}")
session = x.session
shape_x = tuple(x.shape)
shape_y = tuple(y.shape)
args = [list(el) + [op_str] for el in zip(x.share_ptrs, y.share_ptrs)]
try:
mask = parallel_execution(spdz_mask, session.parties)(args)
except EmptyPrimitiveStore:
CryptoPrimitiveProvider.generate_primitives(
f"beaver_{op_str}",
session=session,
g_kwargs={
"session": session,
"a_shape": shape_x,
"b_shape": shape_y,
"nr_parties": session.nr_parties,
**kwargs_,
},
p_kwargs={
"a_shape": shape_x,
"b_shape": shape_y
},
)
mask = parallel_execution(spdz_mask, session.parties)(args)
# zip on pointers is compute intensive
mask_local = [mask[idx].get() for idx in range(session.nr_parties)]
eps_shares, delta_shares = zip(*mask_local)
eps_plaintext = ShareTensor.reconstruct(eps_shares)
delta_plaintext = ShareTensor.reconstruct(delta_shares)
# Specific arguments to each party
args = [[str(remote_session_uuid), eps_plaintext, delta_plaintext, op_str]
for remote_session_uuid in session.rank_to_uuid.values()]
shares = parallel_execution(mul_parties, session.parties)(args, kwargs_)
result = MPCTensor(shares=shares, session=session)
return result
