Privacy-Preserving Machine Learning for User Data

This project aims to develop a machine learning model that can analyze sensitive user data while ensuring privacy. We implemented techniques such as differential privacy and federated learning to allow for predictive text input and personalized recommendations without compromising user data security.

Key Technologies

• Languages: Python
• Libraries: TensorFlow, PyTorch
• Tools: CoreML, Jupyter

Project Structure

• data/: Contains raw and processed data.
• notebooks/: Jupyter notebooks for data preprocessing and model training.
• src/: Source code for implementing differential privacy, federated learning, and the model.
• tests/: Unit tests for the implemented methods.
• requirements.txt: Python dependencies.

Setup

1. Clone the repository:

   git clone https://github.com/abhisekjha/privacy-preserving-ml.git
   cd privacy-preserving-ml

2. Install dependencies:

   pip install -r requirements.txt

Usage

1. Preprocess the data:

   jupyter notebook notebooks/data_preprocessing.ipynb

2. Train the model:

   jupyter notebook notebooks/model_training.ipynb

Impact

• Enabled secure and private analysis of user data.
• Demonstrated compliance with strict privacy standards.
• Improved user trust and satisfaction with privacy-first features.

License

This project is licensed under the MIT License.