secure-multiparty-computation

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

This is the development repository for the OpenFHE library. The current (stable) version is v1.2.4 (released on March 21, 2025).

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

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.

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

Cloud native Secure Multiparty Computation Stack

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.

Updatable Private Set Intersection Revisited: Extended Functionalities, Deletion, and Worst-Case Complexity (Asiacrypt 2024)

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

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

Secure Computation Utilities

SecretFlow-Serving is a serving system for privacy-preserving machine learning models.

Platform for building 2PC garbled circuits

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

Fault-tolerant secure multiparty computation in Python.