def przs_generate_random_share(
self,
shape: Union[tuple, torch.Size],
generators: List[torch.Generator],
) -> Any:
"""Generate a random share using the two generators hold by a party.
Args:
shape (Union[tuple, torch.Size]): Shape.
generators (List[torch.Generator]): Torch generator.
Returns:
Any: ShareTensor
"""
from sympc.tensor import ShareTensor
gen0, gen1 = generators
current_share = generate_random_element(
tensor_type=self.tensor_type,
generator=gen0,
shape=shape,
)
next_share = generate_random_element(
tensor_type=self.tensor_type,
generator=gen1,
shape=shape,
)
share = ShareTensor(session=self)
share.tensor = current_share - next_share
return share
def _generate_random_share(
self, shape: Union[tuple, torch.Size], ring_size: int
) -> List[torch.Tensor]:
"""Generate random tensor share for the given shape and ring_size.
Args:
shape (Union[tuple, torch.Size]): Shape for the share.
ring_size (int): ring size to generate share.
The generators are invoked in Counter(CTR) mode as parties with the same initial seeds
could generate correlated random numbers on subsequent invocations.
Returns:
List[torch.Tensor] : shares generated by the generators.
"""
from sympc.tensor import PRIME_NUMBER
gen1, gen2 = self.przs_generators
tensor_type = get_type_from_ring(ring_size)
max_val = PRIME_NUMBER if ring_size == PRIME_NUMBER else None
tensor_share1 = generate_random_element(
tensor_type=tensor_type, generator=gen1, shape=shape, max_val=max_val
)
tensor_share2 = generate_random_element(
tensor_type=tensor_type, generator=gen2, shape=shape, max_val=max_val
)
return [tensor_share1, tensor_share2]
def _generate_random_share(
self,
shape: Union[tuple, torch.Size],
) -> List[torch.Tensor]:
"""Generate random tensor share for the given shape.
Args:
shape (Union[tuple, torch.Size]): Shape for the share.
The generators are invoked in Counter(CTR) mode as parties with the same initial seeds
could generate the same random number on subsequent invocations.
Returns:
List[torch.Tensor] : shares generated by the generators.
"""
gen1, gen2 = self.przs_generators
tensor_share1 = generate_random_element(
tensor_type=self.tensor_type,
generator=gen1,
shape=shape,
)
tensor_share2 = generate_random_element(
tensor_type=self.tensor_type,
generator=gen2,
shape=shape,
)
return [tensor_share1, tensor_share2]
def test_przs_share_tensor() -> None:
"""Test przs_generate_random_share method from Session for ShareTensor."""
session = Session() # default protocol: FSS
SessionManager.setup_mpc(session)
seed1 = secrets.randbits(32)
seed2 = secrets.randbits(32)
gen1 = get_new_generator(seed1)
gen2 = get_new_generator(seed2)
session.przs_generators = [gen1, gen2]
shape = (2, 1)
share = session.przs_generate_random_share(shape=shape)
assert isinstance(share, ShareTensor)
new_gen1 = get_new_generator(seed1)
new_gen2 = get_new_generator(seed2)
share1 = generate_random_element(generator=new_gen1,
shape=shape,
tensor_type=session.tensor_type)
share2 = generate_random_element(generator=new_gen2,
shape=shape,
tensor_type=session.tensor_type)
target_tensor = share1 - share2
assert (share.tensor == target_tensor).all()
def przs_generate_random_share(
self,
shape: Union[tuple, torch.Size],
) -> Any:
"""Generate a random share using the two generators hold by a party.
Args:
shape (Union[tuple, torch.Size]): Shape for the share.
Returns:
Any: ShareTensor
"""
from sympc.tensor import ShareTensor
gen0, gen1 = self.przs_generators
current_share = generate_random_element(
tensor_type=self.tensor_type,
generator=gen0,
shape=shape,
)
next_share = generate_random_element(
tensor_type=self.tensor_type,
generator=gen1,
shape=shape,
)
# It has encoder_precision = 0 such that the value would not be encoded
share = ShareTensor(
data=current_share - next_share,
session_uuid=self.uuid,
config=Config(encoder_precision=0),
)
return share
def test_przs_rs_tensor() -> None:
"""Test przs_generate_random_share method from Session for ReplicatedSharedTensor."""
falcon = Falcon(security_type="malicious")
session = Session(protocol=falcon)
SessionManager.setup_mpc(session)
seed1 = secrets.randbits(32)
seed2 = secrets.randbits(32)
gen1 = get_new_generator(seed1)
gen2 = get_new_generator(seed2)
session.przs_generators = [gen1, gen2]
shape = (2, 1)
share = session.przs_generate_random_share(shape=shape)
assert isinstance(share, ReplicatedSharedTensor)
new_gen1 = get_new_generator(seed1)
new_gen2 = get_new_generator(seed2)
share1 = generate_random_element(generator=new_gen1,
shape=shape,
tensor_type=session.tensor_type)
share2 = generate_random_element(generator=new_gen2,
shape=shape,
tensor_type=session.tensor_type)
target_tensor = share1 - share2
assert (share.shares[0] == target_tensor).all()