A unified framework for privacy-preserving data analysis and machine learning

Versatile framework for multi-party computation

Apache Teaclave (incubating) is an open source universal secure computing platform, making computation on privacy-sensitive data safe and simple.

This is the development repository for the OpenFHE library. The current (stable) version is v1.1.4 (released on March 8, 2024).

A Privacy-Preserving Framework Based on TensorFlow

MPyC: Multiparty Computation in Python

SPU (Secure Processing Unit) aims to be a provable, measurable secure computation device, which provides computation ability while keeping your private data protected.

A privacy preserving NLP framework

Synergistic fusion of privacy-enhancing technologies for enhanced privacy protection.

Cloud native Secure Multiparty Computation Stack

Kuscia(Kubernetes-based Secure Collaborative InfrA) is a K8s-based privacy-preserving computing task orchestration framework.

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.

Secure Federated Learning Framework with Encryption Aggregation and Integer Encoding Method.

Minimal pure-Python implementation of Shamir's Secret Sharing scheme.

Secure Computation Utilities

A golang MPC framework that can compile Javascript files into garbled circuits

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.

Centralized asynchronous secure aggregation using Shamir's secret sharing for the Boston Women's Workforce Council.