[NOTE] This repository is currently not maintained, so please refer yamanalab/latency-aware-cnn-inference.
Privacy preserving CNN inference over homomorphic encryption (using Microsoft SEAL version 3.6.6)
Supports both latency-oriented single image inference (channel-wise packing) and throughput-oriented multiple images inference (batch-axis packing).
seal-inference-experiment/
├── Dockerfile
├── build_docker.sh
│
├── bin/ # Generated executable files
│
├── datasets/
│ ├── cifar-10/
│ └── mnist/
│
├── include/ # Library header files
│
├── secrets/ # Generated keys and parameter of SEAL (by gen_keys.cpp)
│
├── src/ # Codes for secure inference (C++)
│ ├── cnn/
│ ├── gen_keys.cpp
│ ├── main.cpp
│ └── utils/
│
└── train_model/ # Codes for training model w/ PyTorch (Python)
├── cifar-10/
│ ├── *.py
│ └── saved_models # Model structure(.json) and parameters(.h5)
├── mnist/
│ ├── *.py
│ └── saved_models # Model structure(.json) and parameters(.h5)
├── poetry.lock
├── pyproject.toml
└── utils/
- MNIST (http://yann.lecun.com/exdb/mnist/)
- CIFAR-10 (https://www.cs.toronto.edu/~kriz/cifar.html)
You can build program for secure inference using Docker
$ bash build_docker.sh
You can install required packages for training through Poetry
$ cd train_model
$ poetry install
- Start docker container
$ docker run --privileged -it seal-inference-experiment /bin/bash
- Generate keys (gen_keys.cpp)
$ ./bin/gen_keys -N 16384 -L 5 --q0 50 --qi 30 --ql 60 --prefix N16384_L5_50-30-60 --dataset mnist
- Execute secure inference program (main.cpp)
$ OMP_NUM_THREADS=72 ./bin/main -P N16384_L5_50-30-60 -D mnist -M 3layer_cnn-square-BN --model-params 3layer_cnn-square-BN-99.35_200epoch-1223_1032-params.h5 -A square --fuse-layer --mode single --images 20