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GPU-accelerated CKKS Homomorphic Encryption for PyTorch

Run trained PyTorch models on encrypted data — no decryption needed, no privacy compromised.
Built on OpenFHE with CUDA acceleration.

Traditional machine learning requires access to raw input data — a privacy risk for sensitive domains like healthcare, finance, and biometrics. CuKKS lets you deploy models that never see plaintext:

User: encrypt(input) → [ciphertext] → Server: model([ciphertext]) → [encrypted output] → User: decrypt

The server performs full inference without ever decrypting the data. CuKKS makes this practical with:

• One-line conversion — cukks.convert(model) transforms any trained PyTorch model
• GPU acceleration — CUDA-accelerated HE operations via OpenFHE
• 37 layer types — from Linear and Conv2d to Attention, GroupNorm, and ConvTranspose2d

import torch.nn as nn
import cukks

# 1. Train your model normally
model = nn.Sequential(nn.Linear(784, 128), nn.ReLU(), nn.Linear(128, 10))

# 2. Convert to encrypted model
enc_model, ctx = cukks.convert(model)

# 3. Run encrypted inference
enc_input = ctx.encrypt(test_input)
enc_output = enc_model(enc_input)
output = ctx.decrypt(enc_output)  # Same result, never decrypted on server

pip install cukks[cu121]  # Match your PyTorch CUDA version: cu118, cu121, cu124, cu128

Not sure which CUDA version? Run python -c "import torch; print(torch.version.cuda)".

docker run --gpus all -it pytorch/pytorch:2.2.0-cuda12.1-cudnn8-runtime bash
pip install cukks[cu121]

pip install cukks
cukks-install-backend  # Auto-detects PyTorch CUDA and installs matching backend

git clone https://github.com/devUuung/CuKKS.git && cd CuKKS
pip install -e .

# Build OpenFHE backend
cd openfhe-gpu-public && mkdir build && cd build
cmake .. -DWITH_CUDA=ON && make -j$(nproc)

cd ../../bindings/openfhe_backend
pip install -e .

No model rewriting. No custom HE code. Just call cukks.convert(model):

# Any PyTorch model — MLP, CNN, Transformer — converts automatically
enc_model, ctx = cukks.convert(model, activation_degree=4)

BatchNorm folding, BSGS matrix multiplication, and CNN optimizations are applied automatically.

Full layer table →

All core HE operations run on GPU:

CKKS only supports polynomial operations. CuKKS approximates non-polynomial activations (ReLU, GELU, SiLU, etc.) using Chebyshev polynomial fitting:

# Default: degree-4 (good accuracy / depth balance)
enc_model, ctx = cukks.convert(model)

# Higher degree for better accuracy (costs more depth)
enc_model, ctx = cukks.convert(model, activation_degree=8)

Process multiple samples in a single ciphertext:

samples = [torch.randn(784) for _ in range(8)]
enc_batch = ctx.encrypt_batch(samples)
enc_output = enc_model(enc_batch)
outputs = ctx.decrypt_batch(enc_output, sample_shape=(8,))

# MNIST classification (MLP)
python examples/mnist_encrypted.py --hidden 64 --samples 5

# UNet-style segmentation (ConvTranspose2d, AdaptiveAvgPool2d, Upsample)
python examples/unet_encrypted.py --samples 2

# ResNet-style classification (GroupNorm, AdaptiveAvgPool2d)
python examples/resnet_encrypted.py --samples 1

# Transformer-style NLP (Embedding, LayerNorm)
python examples/transformer_encrypted.py --samples 2

See examples/ for full scripts.

CuKKS includes a benchmark suite for measuring encrypted inference performance:

# Run all benchmarks
python benchmarks/run_benchmarks.py

# Benchmark a specific model
python benchmarks/run_benchmarks.py --model mlp

# Save results to JSON
python benchmarks/run_benchmarks.py --output results.json

Supported models:

Example output:

Model           Plain (ms)   Encrypted (ms)  Overhead   MAE       
--------------------------------------------------------------
mlp             0.01         84.16           6858      x 0.075594
cnn             0.03         2820.15         94053     x 0.048294
resnet          0.04         11444.55        269018    x 0.098026
transformer     0.01         54.27           7794      x 0.087978

Note: Benchmarks require a GPU backend with OpenFHE. Run on a machine with CUDA support for accurate timing.

• API Reference
• CKKS Concepts
• GPU Acceleration Guide
• STIP Packed Attention
• Examples Overview
• 한국어 문서

Apache License 2.0

@software{cukks,
  title = {CuKKS: PyTorch-compatible Encrypted Deep Learning},
  year = {2024},
  url = {https://github.com/devUuung/CuKKS}
}

• OpenFHE — Underlying HE library
• Microsoft SEAL — Alternative HE library

• Homomorphic Encryption for Arithmetic of Approximate Numbers — Cheon et al. (CKKS)
• Bootstrapping for Approximate Homomorphic Encryption — Cheon et al.
• Faster Homomorphic Linear Transformations in HElib — Halevi & Shoup (BSGS)
• GAZELLE: A Low Latency Framework for Secure Neural Network Inference — Juvekar et al. (Convolution)
• PP-STAT: An Efficient Privacy-Preserving Statistical Analysis Framework using Homomorphic Encryption — Choi (Encrypted Statistics)
• STIP: Efficient and Secure Non-Interactive Transformer Inference via Compact Packing — Wang et al. (Packed Attention)
• Efficient Bootstrapping for Approximate Homomorphic Encryption — Bossuat et al. (Double-Hoisting)
• On the Number of Nonscalar Multiplications Necessary to Evaluate Polynomials — Paterson & Stockmeyer (Polynomial Evaluation)