WCCI 2022

The notebooks for the tutorial "Privacy-preserving machine and deep learning with homomorphic encryption: an introduction" can be found in the folder "WCCI2022"! The notebook Tutorial_to_fill.ipynb is the suggested one. We will complete and run it during the tutorial. You can also run it directly on Google Colab, to avoid compatibility issues at this link!.

For reference, the Tutorial_complete.ipynb notebook already contains the final version of the notebook with a deeper explanation. Also this one can be run on Colab: link.

Introduction-to-BFV-HE-ML

This repository contains the code used in the paper "Privacy-preserving deep learning with homomorphic encryption: an introduction".

Dependencies:

• PyTorch
• NumPy
• Pyfhel

The repository is organized this way:

• BFV_theory contains a Jupyter notebook (BFV_theory.ipynb) which introduces the reader to the BFV Homomorphic Encryption scheme, along with a (gentle) introduction to the main math concepts of the scheme, together with an implementation of the scheme made with Python, from scratch, using NumPy. This scheme construction should help the reader gain a deeper understanding of the scheme, before using more advanced and ready-to-use libraries (like SEAL, Pyfhel, etc.);
• HE-ML contains a Jupyter notebook (HE-ML.ipynb) which introduces the reader to the concept of Homomorphic-Encryption enabled Machine-Learning. In the notebook a simple CNN, the LeNet-1, will be translated into a model able to work on encrypted data, along with an explanation on the different design choices, as well as on the encryption parameters setting;
• Experiments contains the Python scripts used to obtain the experimental results shown in the paper. Running the scripts and looking at the corresponding file .log, it will be possible to reproduce and check the results.