Differential Privacy in Deep Learning
Evaluating privacy-performance trade-offs using the PATE framework.
A research-focused study on maintaining data privacy without sacrificing model utility. This project explored formal privacy guarantees when training deep learning models on sensitive financial datasets.
Technical Stack
- Frameworks: PyTorch, Opacus (for Differential Privacy).
- Privacy Mechanism: PATE (Private Aggregation of Teacher Ensembles).
- Analysis: Formal Privacy Budgeting ($\epsilon, \delta$ differential privacy).
Core Features
- High-Accuracy Privacy: Achieved 85.5% test accuracy on sensitive financial data while maintaining strict differential privacy guarantees.
- Trade-off Analysis: Led the investigation into how varying noise multipliers and clipping gradients affect the convergence of deep learning models.