https://github.com/nucypher/nufhe

NuCypher fully homomorphic encryption (NuFHE) library implemented in Python
https://github.com/nucypher/nufhe

Last synced: 3 months ago
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NuCypher fully homomorphic encryption (NuFHE) library implemented in Python

• Host: GitHub
• URL: https://github.com/nucypher/nufhe
• Owner: nucypher
• License: gpl-3.0
• Archived: true
• Created: 2018-04-08T05:17:17.000Z (about 6 years ago)
• Default Branch: master
• Last Pushed: 2022-06-21T21:29:13.000Z (almost 2 years ago)
• Last Synced: 2024-03-20T04:20:37.268Z (3 months ago)
• Language: Python
• Homepage: https://nufhe.readthedocs.io/en/latest/
• Size: 444 KB
• Stars: 438
• Watchers: 23
• Forks: 52
• Open Issues: 25
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.md

Lists

• DeFi-Developer-Road-Map - NuFHE - GPU accelerated FHE library (Roadmap)
• awesome-he - NuFHE - GPU-accelerated HE library, faster than cuFHE, that implements the [tfhe](#tfhe) algorithms. (Libraries)
• awesome-fhe - NuFHE - GPU-accelerated HE library, faster than cuFHE, that implements the [tfhe](#tfhe) algorithms. (Libraries / [Sunscreen](https://sunscreen.tech/))
• awesome-stars - nufhe - NuCypher fully homomorphic encryption (NuFHE) library implemented in Python (Python)

README

        
# A GPU implementation of fully homomorphic encryption on torus

This library implements the fully homomorphic encryption algorithm from [`TFHE`](https://github.com/tfhe/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`](https://github.com/vernamlab/cuFHE). Refer to the [project documentation](https://nufhe.readthedocs.io/en/latest/) for more details.

## Usage example

```python

    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

    -