paillier-numpy 0.0.2

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Paillier-Numpy

Introduction

​ This project implements the partially homomorphic Paillier algorithm and extends and optimizes encryption, decryption, matrix multiplication, and other operations on Numpy matrices, thereby enhancing speed.

​ Main reference projects：https://github.com/data61/python-paillier

​ If there is a need for distributed computing, this project contains a Paillier implementation that I rewrote using Dask. It can perform distributed encryption, decryption, and computation using Dask's distribute functionality. However, it is worth noting that this is merely an experimental project.

How to use

Encrypt and decrypt

import numpy as np
from paillier import generate_paillier_keypair

# create public key and private key
p,q = generate_paillier_keypair()

# create random float
data = np.random.random_sample((100, 100))

# encrypt
encry = p.encrypt(data)

# decrypt
decry = q.decrypt(encry)

Ciphertext addition and subtraction

p, q = generate_paillier_keypair()
data = np.random.random((2, 2))
encry = p.encrypt(data)

# float
encry2 = encry + 3.1415926
print(q.decrypt(encry2) == data + 3.1415926)
# [[ True  True]
#  [ True  True]]

# int
encry3 = encry + 666
print(q.decrypt(encry3) == data + 666)
# [[ True  True]
#  [ True  True]]

# matrix float
data2 = np.random.random_sample((2, 2))
encry4 = encry + data2
print(q.decrypt(encry4) == data + data2)
# [[ True  True]
#  [ True  True]]

# matrix int
data2 = np.random.randint(-100,100,(2, 2))
encry5 = encry + data2
print(q.decrypt(encry5) == data + data2)
# [[ True  True]
#  [ True  True]]

Ciphertext Multiplication

p, q = generate_paillier_keypair()
data = np.random.random((2, 2))
encry = p.encrypt(data)

# int
encry2 = encry * 2
print(q.decrypt(encry2)==data*2)
# [[ True  True]
#  [ True  True]]

# float
encry2 = encry * 3.141592
print(q.decrypt(encry2)==data*3.141592)
# [[ True  True]
#  [ True  True]]

Dot

Note: When performing matrix multiplication, there may be slight errors due to precision issues in the 18th decimal place.

p, q = generate_paillier_keypair()
data = np.random.random((2, 2))
data2 = np.random.random_sample((2,3))
encry = p.encrypt(data)

# [A].dot(B)
encry2 = encry.dot(data2)
print(q.decrypt(encry2)==data.dot(data2))
# [[ True  True  True]
#  [ True  True  True]]

# B.dot([A])
# !!!!!!!!!!!!!!!!!!!!!!
# [A] must  be converted to Numpy.ndarray
encry3 = data2.T.dot(encry.toArray())
print(q.decrypt(encry3)==data2.T.dot(data))
# [[ True  True]
#  [ True  True]
#  [ True  True]]

Multi-process parallelism

Note: The sample code is as follows: where partitions is the number of blocks the matrix is divided into for multiprocessing use.

p, q = generate_paillier_keypair()
data = np.random.random((1000, 100))
data2 = np.random.random_sample((1000,100))

encry = p.encrypt(data)
# time : 0:00:00.624028
decry = q.decrypt(encry)
# time : 0:01:06.076635

encry = p.encrypt(data, is_pool=True, partitions=10)
# time : 0:00:00.282844
decry = q.decrypt(encry, is_pool=True, partitions=10)
# time : 0:00:04.417449

DOT by Multi-process parallelism

Note: Recommend using multi-process matrix multiplication

p, q = generate_paillier_keypair()
data = np.random.random((100, 10))
data2 = np.random.random_sample((10,100))

encry = p.encrypt(data)
encry2 = encry.dot(data2)
# time cost: 0:00:21.922967

encry2 = p.encrypt(data, is_pool=True, partitions=10)
encry3 = encry2.dot(data2,is_pool=True,partitions=10)
# time cost: 0:00:01.600504