A unified framework for privacy-preserving data analysis and machine learning
This is the development repository for the OpenFHE library. The current version is 1.5.1 (released on April 10, 2026).
Versatile framework for multi-party computation
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)
Secure Computation Utilities
SecretFlow-Serving is a serving system for privacy-preserving machine learning models.
Curl: Private LLMs through Wavelet-Encoded Look-Up Tables
TypeScript library for working with encrypted data within nilDB queries and replies.
A development framework for Fully Homomorphic Encryption (FHE)
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