OpenFHE-Python-based Examples of Encrypted Support Vector Machine Inference based on this C++ example.
Before running any code from this repo, make sure you have a working Python 3.6+ installation. Then, install the dependencies:
1 - PyBind11:
pip install "pybind11[global]" # or alternatively, if you use conda:
conda install -c conda-forge pybind11
2 - OpenFHE-development:
git clone [email protected]:openfheorg/openfhe-development.git
cd openfhe-development
mkdir build
cd build
cmake ..
make -j N # number of processors - 4 in my case
sudo make install && cd ../.. # to get back to original path
3 - OpenFHE-Python:
git clone [email protected]:openfheorg/openfhe-python.git
cd openfhe-python
mkdir build
cd build
cmake .. # or alternatively for conda: cmake .. -DPYTHON_EXECUTABLE_PATH=$CONDA_PREFIX/bin/python
make -j N # number of processors - 4 in my case
sudo make install && cd ../.. # to get back to original path
4 - Model Requirements:
git clone [email protected]:reneroliveira/openfhe-svm.git
cd openfhe-svm
pip install -r requirements.txt
First, install openfhe-python
following the instructions of library's repository.
Then, you can run python model_training.py
to train and save the model weights, and then call the encrypted model files for inference. All necessary data is inside data/
directory, but if you want to reproduce the data generation run the get_data.py
file.
The main files are encrypted_svm_linear.py
and encrypted_svm_poly.py
, which are the encrypted versions of inference of the linear and polynomial kernel SVM models, respectively.