secretflow/secretflow

Health v2 · maintenance only

Flags: prerelease_only

Adoption not part of the score

2713 stars · 470 forks observed · 2026-09-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

SecretFlow is a unified Python framework for privacy-preserving data analysis and machine learning. It layers cryptographic devices (MPC, HE, TEE), a device-flow DAG abstraction, and algorithm/workflow layers for training on horizontally or vertically partitioned data.

Use cases

• train machine learning models on federated data without sharing raw data
• run joint data analysis across parties with secure multiparty computation
• apply differential privacy to model training
• compute private set intersection between two datasets
• do vertical federated learning on feature-partitioned data
• build privacy-preserving data pipelines with hyperparameter tuning

When to choose

• you need multi-party analytics or ML where data cannot leave each party
• you want a single framework covering MPC, HE, TEE, and federated learning
• you need both horizontal and vertical data partitioning support

When to avoid

• you only need simple single-party ML without privacy constraints
• you need a lightweight production deployment without container orchestration
• your team cannot handle the operational complexity of cryptographic protocols

Facets

framework · maturity active

machine-learning cryptography data-science security privacy rag privacy machine-learning data-science security artificial-intelligence python privacy-preserving federated-learning secure-multiparty-computation homomorphic-encryption differential-privacy private-set-intersection trusted-execution-environment split-learning confidential-computing linux docker

Member repositories

For agents

markdown · JSON · MCP: product_card(name="secretflow/secretflow")