MPyC: Multiparty Computation in Python

Privacy -preserving Neural Networks

Minimal pure-Python implementation of a secure multi-party computation (MPC) protocol for evaluating arithmetic sum-of-products expressions via a non-interactive computation phase.

Python library that serves as an API for common cryptographic primitives used to implement OPRF, OT, and PSI protocols.

Docker CLI package for the vantage6 infrastructure

Perform multi-party computation on machine learning applications

Minimal pure-Python implementation of Shamir's secret sharing scheme.

TNO PET Lab - secure Multi-Party Computation (MPC) - Protocols - Secure Risk Propagation

Python library for working with circuit definitions represented in the Bristol Fashion.

Embedded domain-specific combinator library for the abstract assembly and automated synthesis of logical circuits.

TNO PET Lab - secure Multi-Party Computation (MPC) - Communication

Fault-tolerant secure multiparty computation in Python.

Data structure for representing additive secret shares of integers, designed for use within secure multi-party computation (MPC) protocol implementations.

TNO PET Lab - secure Multi-Party Computation (MPC) - Encryption Schemes - Paillier

A POC Python implementation of the Millionaires' problem using Yao's Garbled Circuit protocol.

Python implementation of the TPC protocol from the paper "Authenticated Garbling and Efficient Maliciously Secure Two-Party Computation"

Federated Learning (FL) is a collaborative machine learning approach that enables decentralized data processing. Instead of collecting and storing data in a central server, FL trains machine learning models directly on devices or servers where the data resides, enhancing privacy and security.

TNO PET Lab - secure Multi-Party Computation (MPC) - MPyC - Secure Learning

Oblivious transfer (OT) communications protocol message/response functionality implementations based on Curve25519 and the Ristretto group.

Tooling for writing data-oblivious programs (mpyc, pysnark, ...) using non-oblivious constructs (if/for/...)