def spdz_mul(
cmd: Callable, shares: dict, other_shares, crypto_provider: AbstractWorker, field: int, **kwargs
):
"""Abstractly Multiplies two tensors
Args:
cmd: a callable of the equation to be commuted
shares: a dictionary <location_id -> PointerTensor) of shares corresponding to
self. Equivalent to calling self.child.
other_shares: a dictionary <location_id -> PointerTensor) of shares corresponding
to the tensor being multiplied by self.
crypto_provider: an AbstractWorker which is used to generate triples
field: an interger denoting the size of the field
"""
locations = list(shares.keys())
shares_shape = shares[locations[0]].shape
other_shape = other_shares[locations[0]].shape
triple = crypto_provider.generate_triple(cmd, field, shares_shape, other_shape, locations)
a, b, c = triple
a, b, c = a.child, b.child, c.child
d = {}
e = {}
for location in locations:
d[location] = shares[location] - a[location]
e[location] = other_shares[location] - b[location]
delta = torch.zeros(shares_shape, dtype=torch.long)
epsilon = torch.zeros(other_shape, dtype=torch.long)
for location in locations:
d_temp = d[location].get()
e_temp = e[location].get()
delta = delta + d_temp
epsilon = epsilon + e_temp
delta_epsilon = cmd(delta, epsilon)
delta_ptrs = {}
epsilon_ptrs = {}
a_epsilon = {}
delta_b = {}
z = {}
for location in locations:
delta_ptrs[location] = delta.send(location)
epsilon_ptrs[location] = epsilon.send(location)
a_epsilon[location] = cmd(a[location], epsilon_ptrs[location])
delta_b[location] = cmd(delta_ptrs[location], b[location])
z[location] = a_epsilon[location] + delta_b[location] + c[location]
delta_epsilon_pointer = delta_epsilon.send(locations[0])
z[locations[0]] = z[locations[0]] + delta_epsilon_pointer
return z
def spdz_mul(cmd: Callable, x_sh, y_sh, crypto_provider: AbstractWorker,
field: int):
"""Abstractly multiplies two tensors (mul or matmul)
Args:
cmd: a callable of the equation to be computed (mul or matmul)
x_sh (AdditiveSharingTensor): the left part of the operation
y_sh (AdditiveSharingTensor): the right part of the operation
crypto_provider (AbstractWorker): an AbstractWorker which is used to generate triples
field (int): an integer denoting the size of the field
Return:
an AdditiveSharingTensor
"""
assert isinstance(x_sh, sy.AdditiveSharingTensor)
assert isinstance(y_sh, sy.AdditiveSharingTensor)
locations = x_sh.locations
# Get triples
a, b, a_mul_b = crypto_provider.generate_triple(cmd, field, x_sh.shape,
y_sh.shape, locations)
delta = x_sh - a
epsilon = y_sh - b
# Reconstruct and send to all workers
delta = delta.reconstruct()
epsilon = epsilon.reconstruct()
delta_epsilon = cmd(delta, epsilon)
# Trick to keep only one child in the MultiPointerTensor (like in SNN)
j1 = torch.ones(delta_epsilon.shape).long().send(locations[0], **no_wrap)
j0 = torch.zeros(delta_epsilon.shape).long().send(*locations[1:],
**no_wrap)
if len(locations) == 2:
j = sy.MultiPointerTensor(children=[j1, j0])
else:
j = sy.MultiPointerTensor(children=[j1] + j0.child.values())
delta_b = cmd(delta, b)
a_epsilon = cmd(a, epsilon)
return delta_epsilon * j + delta_b + a_epsilon + a_mul_b
