Privacy-Preserving ML Framework

A comprehensive framework for building privacy-preserving machine learning systems using various cryptographic techniques.

🛠️ Privacy-Preserving ML Framework

A comprehensive framework for building privacy-preserving machine learning systems that combines multiple cryptographic techniques to enable secure and private AI applications.

🎯 Project Overview

This framework provides researchers and developers with the tools needed to implement privacy-preserving machine learning systems using state-of-the-art cryptographic protocols. It supports federated learning, differential privacy, secure aggregation, and homomorphic encryption.

🔬 Key Components

Federated Learning Module

  • Secure Aggregation: MPC-based model aggregation protocols
  • Privacy Auditing: Tools for analyzing privacy guarantees
  • Performance Optimization: Efficient communication and computation

Differential Privacy Implementation

  • Noise Mechanisms: Laplace, Gaussian, and exponential mechanisms
  • Composition Theorems: Advanced composition and post-processing
  • Privacy Budget Management: Automatic privacy budget tracking

Homomorphic Encryption Integration

  • TFHE Support: Integration with TFHE-rs library
  • Model Encryption: Secure model training and inference
  • Performance Benchmarks: Comprehensive performance analysis

💡 Applications

  • Healthcare: Secure medical data analysis
  • Finance: Privacy-preserving fraud detection
  • IoT: Secure edge computing and analytics
  • Research: Academic research with privacy guarantees

This framework enables researchers and practitioners to build privacy-preserving machine learning systems without deep cryptographic expertise.