#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 stable version is 1.2.4 (released on March 21, 2025). The current development version is 1.3.0 (released on May 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
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)
#计算机科学#Curl: Private LLMs through Wavelet-Encoded Look-Up Tables
Minimal pure-Python implementation of Shamir's secret sharing scheme.
#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.