def to_paillier(element, public_key):
if isinstance(element, torch.Tensor) and isinstance(
element.child, PaillierTensor):
element.child.pubkey = public_key
child = element.child.child
if isinstance(child, ndarray):
return element
elif isinstance(child, EncryptedNumber):
element.child.child = array([child])
return element
else:
raise Exception(
"The tensor does not have an EncryptedNumber or a np.ndarray as child"
)
elif isinstance(element, PaillierTensor):
element.pubkey = public_key
return element.wrap()
elif isinstance(element, ndarray):
tensor = PaillierTensor()
tensor.child = element
tensor.pubkey = public_key
return tensor.wrap()
elif isinstance(element, list):
tensor = PaillierTensor()
tensor.child = array(element)
tensor.pubkey = public_key
return tensor.wrap()
elif isinstance(element, EncryptedNumber):
tensor = PaillierTensor()
tensor.child = array([element])
tensor.pubkey = public_key
return tensor.wrap()
else:
raise TypeError(type(element))
def deserialize_paillier(struct, pub=None):
# Case 1: dict recursion
if isinstance(struct, dict):
pub = PaillierPublicKey(n=int(struct['n']))
child = [
deserialize_paillier(substruct, pub)
for substruct in struct['values']
]
# Building Paillier Tensor
tensor = PaillierTensor()
tensor.child = array(child)
tensor.pubkey = pub
return tensor.wrap()
# Case 2: list recursion
elif isinstance(struct, list):
return [deserialize_paillier(substruct, pub) for substruct in struct]
# Case 3: Tuple deserialization
elif isinstance(struct, tuple):
return EncryptedNumber(pub, int(struct[0]), int(struct[1]))
# Case 4: Unknown type
else:
raise TypeError(type(struct))