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SecretFlow is a unified framework for privacy-preserving data intelligence and machine learning. To achieve this goal, it provides:

• An abstract device layer consists of plain devices and secret devices which encapsulate various cryptographic protocols.
• A device flow layer modeling higher algorithms as device object flow and DAG.
• An algorithm layer to do data analysis and machine learning with horizontal or vertical partitioned data.
• A workflow layer that seamlessly integrates data processing, model training, and hyperparameter tuning.

• SecretFlow
  • Getting Started
  • Components
  • API Reference
  • Tutorial

• SCQL: A system that allows multiple distrusting parties to run joint analysis without revealing their private data.
• SPU: A provable, measurable secure computation device, which provides computation ability while keeping your private data protected.
• HEU: A high-performance homomorphic encryption algorithm library.
• YACL: A C++ library that contains cryptgraphy, network and io modules which other SecretFlow code depends on.

Please check INSTALLATION.md

Please check DEPLOYMENT.md

We also provide a curated list of papers and SecretFlow's tutorials on Privacy-Enhancing Technologies (PETs).

Please check AWESOME-PETS.md

Please check CONTRIBUTING.md

Non-release versions of SecretFlow are prohibited to use in any production environment due to possible bugs, glitches, lack of functionality, security issues or other problems.