/homomorphic-encryption-MLops

Primary LanguagePythonMIT LicenseMIT

homomorphic-encryption-MLops

This repository demonstrates a simple example of securing a machine learning model using homomorphic encryption.

  • The example involves training a basic machine learning model for predicting outcomes, and then integrating homomorphic encryption to make predictions on encrypted data.

Instructions

  1. Run the Training Script:

    • Navigate to the src directory.
    • Execute python train_ml_model.py to train a simple machine learning model.

  2. Load the Trained Model:

    • Load your own trained machine learning model.
    • Modify encrypt_predict.py accordingly, replacing the placeholder model = None with your loaded model.

  3. Run the Homomorphic Encryption Script:

    • Execute python encrypt_predict.py to make predictions on encrypted data using homomorphic encryption.

Adjust the code as needed for your specific model and data. The folder structure and dependencies are provided for easy setup.

Folder Structure

homomorphic_ml_example/
|-- src/
|   |-- train_ml_model.py
|   |-- encrypt_predict.py
|-- requirements.txt