secure-multiparty-computation

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

Versatile framework for multi-party computation

This is the development repository for the OpenFHE library. The current development version is 1.4.0 (released on August 18, 2025). The current stable version is 1.3.1 (released on July 11, 2025).

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.

HEonGPU is a high-performance library that optimizes Fully Homomorphic Encryption (FHE) on GPUs. Leveraging GPU parallelism, it reduces computational load through concurrent execution. Its multi-stream architecture minimizes data transfer overhead, making it ideal for large-scale encrypted computations with reduced latency.

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)

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

TypeScript library for working with encrypted data within nilDB queries and replies.

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

Secure Computation Utilities

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

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