Secure Computation Protocol Library

A comprehensive library of secure computation protocols for privacy-preserving data analysis and machine learning.

🔐 Secure Computation Protocol Library

A comprehensive library implementing various secure computation protocols for privacy-preserving data analysis and machine learning. This library provides researchers and developers with ready-to-use implementations of state-of-the-art cryptographic protocols.

🎯 Project Overview

Secure computation enables multiple parties to compute functions on their private data without revealing the data itself. This library provides implementations of various protocols, from simple secure aggregation to complex multi-party machine learning algorithms.

🔬 Key Components

Secure Multiparty Computation (MPC)

  • Garbled Circuits: Boolean circuit evaluation
  • Secret Sharing: Arithmetic and boolean secret sharing
  • Oblivious Transfer: 1-out-of-N oblivious transfer protocols
  • Secure Comparison: Privacy-preserving comparison operations

Homomorphic Encryption

  • TFHE Integration: Fully homomorphic encryption support
  • BFV Implementation: Brakerski-Fan-Vercauteren scheme
  • CKKS Support: Approximate homomorphic encryption
  • Performance Optimization: Optimized parameter selection

Zero-Knowledge Proofs

  • zk-SNARKs: Succinct non-interactive arguments
  • Bulletproofs: Efficient range proofs
  • Sigma Protocols: Interactive proof systems
  • Proof Composition: Combining multiple proofs

💡 Applications

  • Privacy-Preserving ML: Secure model training and inference
  • Secure Aggregation: Privacy-preserving statistics
  • Blockchain Privacy: Confidential transactions and smart contracts
  • IoT Security: Secure device communication and computation

This library makes advanced cryptographic protocols accessible to researchers and developers working on privacy-preserving applications.