youben11

"""A basic homomorphic encryption scheme inspired from BFV https://eprint.iacr.org/2012/144.pdf

You can read my blog post explaining the implementation details here: https://www.ayoub-benaissa.com/blog/build-he-scheme-from-scratch-python/

Disclaimer: This implementation doesn’t neither claim to be secure nor does it follow software engineering best practices,
it is designed as simple as possible for the reader to understand the concepts behind homomorphic encryption schemes.
"""

import numpy as np
from numpy.polynomial import polynomial as poly

youben11

import hnumpy as hnp
import numpy as np

youben11

weights = np.array([0.1, 0.2, 0.3, 0.4, 0.5])
bias = np.array([0.1])


def sigmoid(x):
    return 1 / (1 + np.exp(-x))

  
def func(x):
    return sigmoid(np.dot(x, weights) + bias)

youben11

h = hnp.compile_fhe(
    func,
    {
        'x': hnp.encrypted_ndarray(bounds=(-1, 1), shape=(5,)),
    }
)

youben11

x = np.random.uniform(-1, 1, 5)

youben11

print(f"Simulation result: {h.simulate(x)}")
print(f"Plain NumPy result: {func(x)}")

youben11

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

youben11

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

youben11

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

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()