Implementation of protocols in the paper titled "Scalable Multi-Party Computation Protocols for Machine Learning in the Honest-Majority Setting"

Enabling PyTorch on XLA Devices (e.g. Google TPU)

🦜🔗 Build context-aware reasoning applications

TeeApps contain a general framework for developing TEE applications and various application implementations used in federated AI/BI.

A privacy-preserving computing system based on TEE.

TAOISM: A TEE-based Confidential Heterogeneous Deployment Framework for DNN Models

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

Ancillary open source software to support confidential computing on NVIDIA GPUs

A high-performance homomorphic encryption algorithm library.

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

Website for Artifact Evaluation at EuroSys, SOSP, OSDI, ATC

🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.

A list of system papers using/about Intel SGX

llm deploy project based mnn.

MNN is a blazing fast, lightweight deep learning framework, battle-tested by business-critical use cases in Alibaba

SCQL (Secure Collaborative Query Language) is a system that allows multiple distrusting parties to run joint analysis without revealing their private data.

(Cir)cuit (C)ompiler. Compiling high-level languages to circuits for SMT, zero-knowledge proofs, and more.

⭐ A Curated List of Awesome WebAssembly Applications

SGX从入门到放弃

A curated list of open-source projects that help exploit Intel SGX technology

ABY - A Framework for Efficient Mixed-protocol Secure Two-party Computation

The seL4 microkernel

🚀 JavaScript diagramming library that uses SVG and HTML for rendering.

Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more

A native gRPC client & server implementation with async/await support.

The Torch-MLIR project aims to provide first class support from the PyTorch ecosystem to the MLIR ecosystem.

Training and serving large-scale neural networks with auto parallelization.

Tensors and Dynamic neural networks in Python with strong GPU acceleration

Versatile framework for multi-party computation