paweld2 / nufhe

NuCypher fully homomorphic encryption (NuFHE) library implemented in Python

A GPU implementation of fully homomorphic encryption on torus

This library implements the fully homomorphic encryption algorithm from TFHE using CUDA and OpenCL. Unlike TFHE, where FFT is used internally to speed up polynomial multiplication, nufhe can use either FFT or purely integer NTT (DFT-like transform on a finite field). The latter is based on the arithmetic operations and NTT scheme from cuFHE. Refer to the project documentation for more details.

Usage example

    import random
    import nufhe

    size = 32
    bits1 = [random.choice([False, True]) for i in range(size)]
    bits2 = [random.choice([False, True]) for i in range(size)]
    reference = [not (b1 and b2) for b1, b2 in zip(bits1, bits2)]

    ctx = nufhe.Context()
    secret_key, cloud_key = ctx.make_key_pair()

    ciphertext1 = ctx.encrypt(secret_key, bits1)
    ciphertext2 = ctx.encrypt(secret_key, bits2)

    vm = ctx.make_virtual_machine(cloud_key)
    result = vm.gate_nand(ciphertext1, ciphertext2)
    result_bits = ctx.decrypt(secret_key, result)

    assert all(result_bits == reference)

Performance

Platform	Library	Performance (ms/bit)
		Binary Gate	MUX Gate
Single Core/Single GPU - FFT	TFHE (CPU)	13	26
	nuFHE	0.13	0.22
	Speedup	100.9	117.7
Single Core/Single GPU - NTT	cuFHE	0.35	N/A
	nuFHE	0.35	0.67
	Speedup	1.0	-