#计算机科学#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 version is 1.3.1 (released on July 11, 2025).
A Privacy-Preserving Framework Based on TensorFlow
SPU (Secure Processing Unit) aims to be a provable, measurable secure computation device, which provides computation ability while keeping your private data protected.
#自然语言处理#A privacy preserving NLP framework
Synergistic fusion of privacy-enhancing technologies for enhanced privacy protection.
#计算机科学#Kuscia(Kubernetes-based Secure Collaborative InfrA) is a K8s-based privacy-preserving computing task orchestration framework.
Cloud native Secure Multiparty Computation Stack
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-strea...
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.
#计算机科学#Curl: Private LLMs through Wavelet-Encoded Look-Up Tables
#计算机科学#Secure Federated Learning Framework with Encryption Aggregation and Integer Encoding Method.
#计算机科学#SecretFlow-Serving is a serving system for privacy-preserving machine learning models.