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https://github.com/khoaguin/hesplitnet
Two-party Privacy-preserving Neural Network Training using Split Learning and Homomorphic Encryption (CKKS Scheme)
https://github.com/khoaguin/hesplitnet
Last synced: 16 days ago
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Two-party Privacy-preserving Neural Network Training using Split Learning and Homomorphic Encryption (CKKS Scheme)
- Host: GitHub
- URL: https://github.com/khoaguin/hesplitnet
- Owner: khoaguin
- Created: 2022-04-07T03:30:15.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2024-03-13T10:04:15.000Z (8 months ago)
- Last Synced: 2024-10-03T18:19:28.094Z (about 1 month ago)
- Language: Jupyter Notebook
- Homepage:
- Size: 48.9 MB
- Stars: 9
- Watchers: 4
- Forks: 2
- Open Issues: 1
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Metadata Files:
- Readme: README.md
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README
# HESplitNet
Two-party Privacy-preserving Neural Network Training using Split Learning and Homomorphic Encryption in CKKS Scheme.
Source code of the paper [Split Without a Leak: Reducing Privacy Leakage in Split Learning (accepted at the 19th EAI International Conference on Security and Privacy in Communication Networks (SecureComm’23))](https://arxiv.org/abs/2308.15783).
## Requirements
`python==3.9.7`
`tenseal==0.3.10`
`torch==1.8.1+cu102`
`icecream==2.1.2`
`h5py==3.7.0`
`hydra-core==1.1.1`
`pandas==1.5.2`
## Repository Structure
```
├── conf
│ ├── config.yaml # hold the configurations (dataset to use, hyperparameters)
├── data
│ ├── ptbxl_processing.ipynb # code to process the PTB-XL dataset
├── images # images to be used in notebooks and README.md
├── notebooks # contains jupyter notebooks
├── outputs # will be automatically created after running the protocols
├── hesplitnet # contains the code
| ├── multi-clients # code for multi clients protocol (experimental)
| ├── single-client # code for the single client protocol
└── weights # contains the initial and trained weights
```
## Data
The processed [MIT-BIH dataset](https://physionet.org/content/mitdb/1.0.0/) for the one-client protocol (`mitbih_train.hdf5` and `mitbih_test.hdf5`) and multi-client protocol (`multiclient_mitbih_train.hdf5` and `multiclient_mitbih_test.hdf5`) are in the `data/` folder.
For the [PTB-XL dataset](https://physionet.org/content/ptb-xl/1.0.0/), you can run the file `data/ptbxl_processing.ipynb` to produce the `.hdf5` files, but it is recommended that you just download them [here](https://zenodo.org/record/7006692) and put the `.hdf5` files in the `data` folder.
## Running
1. Create a new conda environment, for example `conda create -n hesplitnet python=3.9.7` and activate it using `conda activate hesplitnet`
2. Install the required packages in the `requirements.txt`, e.g. `pip install -r requirements.txt`
3. Install `hesplitnet` as a package using `pip install -e .`
4. Specify the hyperparamters for your protocol in `conf/config.yaml`
5. Running the protocol:
- In the terminal, run `python hesplitnet/single-client/server.py`. The server will be started and waiting for the client.
- Open a new tab in the terminal and run `python hesplitnet/single-client/client.py`. The training process will start.
- After the training is done, the logs and output files will be saved in the directory `outputs//` where `output_dir` is defined in `conf/config.yaml`.
- After training, go to `notebooks/test_mitbih.ipynb` or `notebooks/test_ptbxl.ipynb` to run and inspect the testing procedure for the MIT-BIH or PTB-XL dataset respectively.