youben11

import os
from Crypto.Cipher import AES

# Can be 16, 24, or 32 bytes
KEY = b"A"*16

############
# ECB mode #
############
aes_ecb = AES.new(KEY, mode=AES.MODE_ECB)

youben11

evaluate("Encryption is awesome")
+ the sentence was positive (original: 99.99%, simulated: 99.99%, actual: 99.99%, difference: 0.00%, took: 33.813 seconds)

youben11

def evaluate(sentence):
    try:
        embedded = encode(sentence)
    except KeyError as error:
        print("! the word", error, "is unknown")
        return
    if embedded.shape[0] > SENTENCE_LENGTH_LIMIT:
        print(f"! the sentence should not contain more than {SENTENCE_LENGTH_LIMIT} tokens")
        return
    padded = np.zeros((SENTENCE_LENGTH_LIMIT, 300))

youben11

context = homomorphic_inferer.create_context()
keys = context.keygen()

youben11

SENTENCE_LENGTH_LIMIT = 5

inferer = Inferer(model)

homomorphic_inferer = hnp.compile_fhe(
    inferer.infer,
    {
        "x": hnp.encrypted_ndarray(bounds=(-1, 1), shape=(SENTENCE_LENGTH_LIMIT, 300))
    },
    config=hnp.config.CompilationConfig(

youben11

class Inferer:
    def __init__(self, model):
        parameters = list(model.lstm.parameters())
        
        W_ii, W_if, W_ig, W_io = parameters[0].split(HIDDEN_SIZE)
        W_hi, W_hf, W_hg, W_ho = parameters[1].split(HIDDEN_SIZE)
        b_ii, b_if, b_ig, b_io = parameters[2].split(HIDDEN_SIZE)
        b_hi, b_hf, b_hg, b_ho = parameters[3].split(HIDDEN_SIZE)
        
        self.W_ii = W_ii.detach().numpy()

youben11

HIDDEN_SIZE = 100


class Model(torch.nn.Module):
    def __init__(self):
        super().__init__()
        self.lstm = torch.nn.LSTM(input_size=300, hidden_size=HIDDEN_SIZE)
        self.fc = torch.nn.Linear(HIDDEN_SIZE, 1)
        self.sigmoid = torch.nn.Sigmoid()

youben11

x_enc= keys.encrypt(x)
res = h.run(keys.public_keys, x_enc)
print(f"Encrypted computation result: {keys.decrypt(res)}")

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print(f"Encrypted computation result: {h.encrypt_and_run(keys, x)}")

youben11

ctx = h.create_context()
keys = ctx.keygen()