Stars
Maliciously-Secure Multi-Party Computation (MPC) Engine using Authenticated Garbling
The Hybrid Public Key Encryption (HPKE) standard in Python
Exploring the physical limits of trusted hardware in the classical and quantum settings to achieve security through physics.
Versatile framework for multi-party computation on the web. Based on the implementation of MP-SPDZ.
An implementation of a verifiable oblivious pseudorandom function (RFC 9497)
Concrete: TFHE Compiler that converts python programs into FHE equivalent
A Solidity library for interacting with fhevm.
TFHE-rs: A Pure Rust implementation of the TFHE Scheme for Boolean and Integer Arithmetics Over Encrypted Data.
A library to generate LaTeX expression from Python code.
Concrete ML: Privacy Preserving ML framework using Fully Homomorphic Encryption (FHE), built on top of Concrete, with bindings to traditional ML frameworks.
A unified framework for privacy-preserving data analysis and machine learning
A Rust library for polynomial commitments
Rust pseudo-random number generator based on AES
Secure distributed dataflow framework for encrypted machine learning and data processing
User-friendly secure computation engine based on secure multi-party computation
An efficient, user-friendly, modular, and extensible framework for mixed-protocol secure multi-party computation with two or more parties
Benchmarks for various multi-party computation frameworks
The Torch-MLIR project aims to provide first class support from the PyTorch ecosystem to the MLIR ecosystem.
Privacy transformations on Spark and Pandas dataframes backed by a simple policy language.
SOON TO BE DEPRECATED - Private machine learning progress