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 v1.2.3 (released on October 30, 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.

Curl: Private LLMs through Wavelet-Encoded Look-Up Tables

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

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

Secure Computation Utilities

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.