def __init__(
self,
shares: Optional[List[Union[float, int, torch.Tensor]]] = None,
config: Config = Config(encoder_base=2, encoder_precision=16),
session_uuid: Optional[UUID] = None,
ring_size: int = 2**64,
):
"""Initialize ReplicatedSharedTensor.
Args:
shares (Optional[List[Union[float, int, torch.Tensor]]]): Shares list
from which RSTensor is created.
config (Config): The configuration where we keep the encoder precision and base.
session_uuid (Optional[UUID]): Used to keep track of a share that is associated with a
remote session
ring_size (int): field used for the operations applied on the shares
Defaults to 2**64
"""
self.session_uuid = session_uuid
self.ring_size = ring_size
self.config = config
self.fp_encoder = FixedPointEncoder(base=config.encoder_base,
precision=config.encoder_precision)
tensor_type = get_type_from_ring(ring_size)
self.shares = []
if shares is not None:
for i in range(len(shares)):
self.shares.append(self._encode(shares[i]).to(tensor_type))
def __init__(
self,
data: Optional[Union[float, int, torch.Tensor]] = None,
config: Config = Config(encoder_base=2, encoder_precision=16),
session_uuid: Optional[UUID] = None,
ring_size: int = 2**64,
) -> None:
"""Initialize ShareTensor.
Args:
data (Optional[Any]): The share a party holds. Defaults to None
config (Config): The configuration where we keep the encoder precision and base.
session_uuid (Optional[UUID]): Used to keep track of a share that is associated with a
remote session
ring_size (int): field used for the operations applied on the shares
Defaults to 2**64
"""
self.session_uuid = session_uuid
self.ring_size = ring_size
self.config = config
self.fp_encoder = FixedPointEncoder(base=config.encoder_base,
precision=config.encoder_precision)
self.tensor: Optional[torch.Tensor] = None
if data is not None:
tensor_type = get_type_from_ring(ring_size)
self.tensor = self._encode(data).to(tensor_type)
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 truncation_algorithm1(
ptr_list: List[torch.Tensor],
shape: torch.Size,
session: Session,
ring_size: int,
config: Config,
) -> List[ReplicatedSharedTensor]:
"""Performs the ABY3 truncation algorithm1.
Args:
ptr_list (List[torch.Tensor]): Tensors to truncate
shape (torch.Size) : shape of tensor values
session (Session) : session the tensor belong to
ring_size (int): Ring size of the underlying tensors.
config (Config): The configuration(base,precision) of the underlying tensors.
Returns:
List["ReplicatedSharedTensor"] : Truncated shares.
"""
tensor_type = get_type_from_ring(ring_size)
rand_value = torch.empty(size=shape,
dtype=tensor_type).random_(generator=gen)
base = config.encoder_base
precision = config.encoder_precision
scale = base**precision
x1, x2, x3 = ptr_list
x1_trunc = x1 >> precision if base == 2 else x1 // scale
x_trunc = (x2 + x3) >> precision if base == 2 else (x2 + x3) // scale
shares = [x1_trunc, x_trunc - rand_value, rand_value]
ptr_list = ReplicatedSharedTensor.distribute_shares(
shares, session, ring_size, config)
return ptr_list
def local_decomposition(
x: ReplicatedSharedTensor,
ring_size: str,
bitwise: bool = False) -> List[List[List[ReplicatedSharedTensor]]]:
"""Performs local decomposition to generate shares of shares.
Args:
x (ReplicatedSharedTensor) : input RSTensor.
ring_size (str) : Ring size to generate decomposed shares in.
bitwise (bool): Perform bit level decomposition on bits if set.
Returns:
List[ReplicatedSharedTensor]: Decomposed shares in the given ring size.
Raises:
ValueError: If RSTensor does not have session uuid.
ValueError: If the exactly three parties are not involved in the computation.
"""
if x.session_uuid is None:
raise ValueError("Input RSTensor should have session_uuid")
session = get_session(x.session_uuid)
if session.nr_parties != NR_PARTIES:
raise ValueError(
"ABY3 local_decomposition algorithm requires 3 parties")
ring_size = int(ring_size)
tensor_type = get_type_from_ring(ring_size)
rank = session.rank
zero = torch.zeros(x.shares[0].shape).type(tensor_type)
# Similar to triples, we have instances for the shares generated.
share_lst: List[List[List[ReplicatedSharedTensor]]] = []
input_rst = []
if bitwise:
ring_bits = get_nr_bits(
session.ring_size) # for bit-wise decomposition
input_rst = [x.bit_extraction(idx) for idx in range(ring_bits)]
else:
input_rst.append(x)
for share in input_rst:
shares = [[zero.clone(), zero.clone()] for i in range(NR_PARTIES)]
shares[rank][0] = share.shares[0].clone().type(tensor_type)
shares[(rank + 1) %
NR_PARTIES][1] = (share.shares[1].clone().type(tensor_type))
rst_sh = []
for i in range(NR_PARTIES):
rst = x.clone()
rst.shares = shares[i]
rst.ring_size = ring_size
rst_sh.append(rst)
share_lst.append(rst_sh)
return share_lst
def test_session_init():
"""Test correct initialisation of the Sessin class."""
# Test default init
session = Session()
assert isinstance(session.uuid, UUID)
assert session.parties == []
assert session.trusted_third_party is None
assert session.crypto_store is None
assert session.protocol is not None
assert isinstance(session.config, Config)
assert session.przs_generators == []
assert session.rank == -1
assert session.session_ptrs == []
assert session.tensor_type == get_type_from_ring(2**64)
assert session.ring_size == 2**64
assert session.min_value == -(2**64) // 2
assert session.max_value == (2**64 - 1) // 2
# Test custom init
uuid = uuid4()
config = Config()
session = Session(parties=["alice", "bob"],
ring_size=2**32,
config=config,
ttp="TTP",
uuid=uuid)
assert session.uuid == uuid
assert session.parties == ["alice", "bob"]
assert session.trusted_third_party == "TTP"
assert session.crypto_store is None
assert session.protocol is not None
assert session.config == config
assert session.przs_generators == []
assert session.rank == -1
assert session.session_ptrs == []
assert session.tensor_type == get_type_from_ring(2**32)
assert session.ring_size == 2**32
assert session.min_value == -(2**32) // 2
assert session.max_value == (2**32 - 1) // 2
def test_session_default_init() -> None:
"""Test correct initialisation of the Sessin class."""
# Test default init
session = Session()
assert session.uuid is None
assert session.parties == []
assert session.trusted_third_party is None
assert session.crypto_store is None
assert session.protocol is not None
assert isinstance(session.config, Config)
assert session.przs_generators == []
assert session.rank == -1
assert session.session_ptrs == []
assert session.tensor_type == get_type_from_ring(2**64)
assert session.ring_size == 2**64
assert session.min_value == -(2**64) // 2
assert session.max_value == (2**64 - 1) // 2
def test_fixed_point(precision, base) -> None:
x = torch.tensor([1.25, 3.301])
shares = [x, x]
rst = ReplicatedSharedTensor(shares=shares,
config=Config(encoder_precision=precision,
encoder_base=base))
fp_encoder = FixedPointEncoder(precision=precision, base=base)
tensor_type = get_type_from_ring(rst.ring_size)
for i in range(len(shares)):
shares[i] = fp_encoder.encode(shares[i]).to(tensor_type)
assert (torch.cat(shares) == torch.cat(rst.shares)).all()
for i in range(len(shares)):
shares[i] = fp_encoder.decode(shares[i].type(torch.LongTensor))
assert (torch.cat(shares) == torch.cat(rst.decode())).all()
def select_shares(x: MPCTensor, y: MPCTensor, b: MPCTensor) -> MPCTensor:
"""Returns either x or y based on bit b.
Args:
x (MPCTensor): input tensor
y (MPCTensor): input tensor
b (MPCTensor): input tensor which is shares of a bit used as selector bit.
Returns:
z (MPCTensor):Returns x (if b==0) or y (if b==1).
Raises:
ValueError: If the selector bit tensor is not of ring size "2".
"""
ring_size = int(b.share_ptrs[0].get_ring_size().get_copy())
shape = b.shape
if ring_size != 2:
raise ValueError(
f"Invalid {ring_size} for selector bit,must be of ring size 2")
if shape is None:
raise ValueError(
"The selector bit tensor must have a valid shape.")
session = x.session
# TODO: Should be made to generate with CryptoProvider in Preprocessing stage.
c_ptrs: List[ReplicatedSharedTensor] = []
for session_ptr in session.session_ptrs:
c_ptrs.append(
session_ptr.prrs_generate_random_share(
shape=shape, ring_size=str(ring_size)))
c = MPCTensor(shares=c_ptrs, session=session,
shape=shape) # bit random share
c_r = ABY3.bit_injection(
c, session, session.ring_size) # bit random share in session ring.
tensor_type = get_type_from_ring(session.ring_size)
mask = (b ^ c).reconstruct(decode=False).type(tensor_type)
d = (mask - (c_r * mask)) + (c_r * (mask ^ 1))
# Order placed carefully to prevent re-encoding,should not be changed.
z = x + (d * (y - x))
return z
def test_session_custom_init() -> None:
config = Config()
session = Session(parties=["alice", "bob"],
ring_size=2**32,
config=config,
ttp="TTP")
assert session.uuid is None
assert session.parties == ["alice", "bob"]
assert session.trusted_third_party == "TTP"
assert session.crypto_store is None
assert session.protocol is not None
assert session.config == config
assert session.przs_generators == []
assert session.rank == -1
assert session.session_ptrs == []
assert session.tensor_type == get_type_from_ring(2**32)
assert session.ring_size == 2**32
assert session.min_value == -(2**32) // 2
assert session.max_value == (2**32 - 1) // 2
def test_private_compare(get_clients, security) -> None:
parties = get_clients(3)
falcon = Falcon(security_type=security)
session = Session(parties=parties, protocol=falcon)
SessionManager.setup_mpc(session)
base = session.config.encoder_base
precision = session.config.encoder_precision
fp_encoder = FixedPointEncoder(base=base, precision=precision)
secret = torch.tensor([[358.85, 79.29], [67.78, 2415.50]])
r = torch.tensor([[357.05, 90], [145.32, 2400.54]])
r = fp_encoder.encode(r)
x = MPCTensor(secret=secret, session=session)
x_b = ABY3.bit_decomposition_ttp(x, session) # bit shares
x_p = [] # prime ring shares
for share in x_b:
x_p.append(ABY3.bit_injection(share, session, PRIME_NUMBER))
tensor_type = get_type_from_ring(session.ring_size)
result = Falcon.private_compare(x_p, r.type(tensor_type))
expected_res = torch.tensor([[1, 0], [0, 1]], dtype=torch.bool)
assert (result.reconstruct(decode=False) == expected_res).all()
def __init__(
self,
parties: Optional[List[Any]] = None,
ring_size: int = 2**64,
config: Optional[Config] = None,
protocol: Optional[Protocol] = None,
ttp: Optional[Any] = None,
) -> None:
"""Initializer for the Session.
Args:
parties (Optional[List[Any]): Used to send/receive messages:
ring_size (int): Field used for the operations applied on the shares
config (Optional[Config]): Configuration used for information needed
by the Fixed Point Encoder. Defaults None
protocol (Optional[str]): Protocol. Defaults None
ttp (Optional[Any]): Trusted third party. Defaults None.
Raises:
ValueError: If protocol is not registered.
"""
# Each worker will have the rank as the index in the list
# Only the party that is the CC (Control Center) will have access
# to this
self.parties: List[Any]
self.nr_parties: int
if parties is None:
self.parties = []
self.nr_parties = 0
else:
self.parties = parties
self.nr_parties = len(parties)
# Some protocols require a trusted third party
# Ex: SPDZ
self.trusted_third_party = ttp
# The CryptoStore is initialized at each party when it is unserialized
self.crypto_store: Optional[Dict[
Any, Any]] = None # TODO: this should be CryptoStore
self.protocol: Protocol = None
if protocol is None:
self.protocol = Protocol.registered_protocols["FSS"]()
else:
if type(protocol).__name__ not in Protocol.registered_protocols:
raise ValueError(f"{type(protocol).__name__} not registered!")
self.protocol = protocol
if (self.parties and len(self.parties) < 3
and self.protocol.security_type == "malicious"):
raise ValueError(
"Malicious security cannot be provided to less than 3 parties")
self.config = config if config else Config()
self.przs_generators: List[Optional[torch.Generator]] = []
# Those will be populated in the setup_mpc
self.rank: int = -1
self.uuid: Optional[UUID] = None
self.session_ptrs = []
self.rank_to_uuid: Dict[int, UUID] = {}
# Ring size
self.tensor_type: Union[torch.dtype] = get_type_from_ring(ring_size)
self.ring_size = ring_size
self.min_value = -(ring_size) // 2
self.max_value = (ring_size - 1) // 2
self.autograd_active = False
def __init__(
self,
parties: Optional[List[Any]] = None,
ring_size: int = 2**64,
config: Optional[Config] = None,
protocol: Optional[str] = "FSS",
ttp: Optional[Any] = None,
uuid: Optional[UUID] = None,
) -> None:
"""Initializer for the Session.
Args:
parties (Optional[List[Any]): Used to send/receive messages:
ring_size (int): Field used for the operations applied on the shares
config (Optional[Config]): Configuration used for information needed
by the Fixed Point Encoder. Defaults None
protocol (Optional[str]): Protocol. Defaults None
ttp (Optional[Any]): Trusted third party. Defaults None.
uuid (Optional[UUID]): Universal Identifier for the session. Defaults None
Raises:
ValueError: If protocol is not registered.
"""
self.uuid = uuid4() if uuid is None else uuid
# Each worker will have the rank as the index in the list
# Only the party that is the CC (Control Center) will have access
# to this
self.parties: List[Any]
self.nr_parties: int
if parties is None:
self.parties = []
self.nr_parties = 0
else:
self.parties = parties
self.nr_parties = len(parties)
# Some protocols require a trusted third party
# Ex: SPDZ
self.trusted_third_party = ttp
# The CryptoStore is initialized at each party when it is unserialized
self.crypto_store: Optional[Dict[
Any, Any]] = None # TODO: this should be CryptoStore
if protocol not in Protocol.registered_protocols:
raise ValueError(f"{protocol} not registered!")
self.protocol: Protocol = Protocol.registered_protocols[protocol]
self.config = config if config else Config()
self.przs_generators: List[List[torch.Generator]] = []
# Those will be populated in the setup_mpc
self.rank = -1
self.session_ptrs: List[Session] = []
# Ring size
self.tensor_type: Union[torch.dtype] = get_type_from_ring(ring_size)
self.ring_size = ring_size
self.min_value = -(ring_size) // 2
self.max_value = (ring_size - 1) // 2
