Ayoub Benaissa youben11

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

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

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

## log_reg.py
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)

## import_hnp.py
import hnumpy as hnp
import numpy as np

## rlwe_he_scheme.py
"""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
	print(f"Simulation result: {h.simulate(x)}")
	print(f"Plain NumPy result: {func(x)}")
	h = hnp.compile_fhe(
	func,
	{
	'x': hnp.encrypted_ndarray(bounds=(-1, 1), shape=(5,)),
	}
	)
	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)
	"""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