This project demonstrates how inference-only Large Language Models (LLMs) can generate synthetic datasets from sensitive data, while ensuring Differential Privacy (DP).
🚀 Hosted on Streamlit Cloud for free and open public access.
- Generate realistic synthetic tabular data
- Apply differential privacy (Laplace noise) during generation
- Download synthetic datasets for safe sharing
- Runs entirely in-browser via Streamlit
git clone https://github.com/yourusername/dp-synth-data.git
cd dp-synth-data
pip install -r requirements.txt
streamlit run app.py