# secretflow/secretflow **Attribution required: if you use, quote, or summarise this content, you must credit and link back to [awesome-repositories.com](https://awesome-repositories.com/repository/secretflow-secretflow).** 2,629 stars · 463 forks · Python · apache-2.0 ## Links - GitHub: https://github.com/secretflow/secretflow - Homepage: https://www.secretflow.org.cn/docs/secretflow/en/ - awesome-repositories: https://awesome-repositories.com/repository/secretflow-secretflow.md ## Topics `confidential-computing` `data-analysis` `differential-privacy` `federated-learning` `homomorphic-encryption` `machine-learning` `privacy-preserving` `private-set-intersection` `secure-multiparty-computation` `split-learning` `trusted-execution-environment` ## Description SecretFlow is a privacy computing framework and platform designed for secure multi-party computation, federated learning, and privacy-preserving data analysis across independent nodes. It provides a management system to coordinate secure workloads and cryptographic tasks across a distributed cluster. The project enables joint data analysis and machine learning on partitioned datasets using cryptographic protocols. It allows for the training of models and the execution of analytical queries across multiple parties without exposing raw source information to any single participant. The framework covers a broad surface of privacy-preserving capabilities, including secure distributed analytics, encrypted data processing, and distributed model development. It incorporates orchestration tools for managing private workflows and coordinating the sequence of computation steps across isolated environments. ## Tags ### Security & Cryptography - [Multi-Party Computation Frameworks](https://awesome-repositories.com/f/security-cryptography/multi-party-computation-frameworks.md) — Provides a comprehensive framework for executing secure multi-party computation across distributed nodes. - [Privacy-Preserving Analysis Tools](https://awesome-repositories.com/f/security-cryptography/data-privacy-tools/privacy-preserving-analysis-tools.md) — Performs analytical queries and data processing across distrusting parties using secure computation. - [Encrypted Data Processing](https://awesome-repositories.com/f/security-cryptography/encrypted-data-processing.md) — Provides capabilities to run analysis algorithms directly on encrypted or partitioned data to keep raw information hidden from the processor. ([source](https://cdn.jsdelivr.net/gh/secretflow/secretflow@main/README.md)) - [Multi-Party Computation](https://awesome-repositories.com/f/security-cryptography/multi-party-computation.md) — Enables joint data analysis across multiple distrusting parties without exposing underlying raw information. - [Privacy Computing Frameworks](https://awesome-repositories.com/f/security-cryptography/privacy-computing-frameworks.md) — Provides a comprehensive framework for secure multi-party computation and privacy-preserving machine learning across distributed nodes. - [Privacy-Preserving Machine Learning](https://awesome-repositories.com/f/security-cryptography/privacy-preserving-machine-learning.md) — Trains machine learning models on distributed datasets while keeping raw source data encrypted. - [Model Partitioning](https://awesome-repositories.com/f/security-cryptography/privacy-preserving-machine-learning/model-partitioning.md) — Splits machine learning model weights and data across parties to ensure no single entity sees the whole dataset. - [Secure Multi-Party Computation Platforms](https://awesome-repositories.com/f/security-cryptography/secure-multi-party-computation-platforms.md) — Provides a platform using cryptographic protocols to analyze joint datasets without exposing raw information. - [Computation Orchestration](https://awesome-repositories.com/f/security-cryptography/multi-party-computation-coordination/computation-orchestration.md) — Coordinates the sequence of distributed computation steps across isolated environments to maintain data sovereignty. - [Private Workflow Management](https://awesome-repositories.com/f/security-cryptography/private-workflow-management.md) — Manages data processing and model training as a unified sequence of operations across secure devices. ([source](https://cdn.jsdelivr.net/gh/secretflow/secretflow@main/README.md)) ### Artificial Intelligence & ML - [Federated Orchestration](https://awesome-repositories.com/f/artificial-intelligence-ml/distributed-model-orchestration/federated-orchestration.md) — Coordinates training tasks and communication for federated learning on partitioned data. - [Privacy-Preserving Model Training](https://awesome-repositories.com/f/artificial-intelligence-ml/privacy-preserving-model-training.md) — Implements training methodologies that keep raw data on local devices while sharing only model updates to maintain privacy. ([source](https://cdn.jsdelivr.net/gh/secretflow/secretflow@main/README.md)) - [Distributed Model Development](https://awesome-repositories.com/f/artificial-intelligence-ml/privacy-preserving-model-training/distributed-model-development.md) — Enables the development of models across partitioned datasets held by different parties without revealing raw information. ([source](https://cdn.jsdelivr.net/gh/secretflow/secretflow@main/README.md)) ### Data & Databases - [Distributed Privacy Queries](https://awesome-repositories.com/f/data-databases/analytical-query-engines/distributed-privacy-queries.md) — Performs analytical queries across multiple distrusting parties without exposing underlying raw data. ([source](https://cdn.jsdelivr.net/gh/secretflow/secretflow@main/README.md)) - [Secure Distributed Analytics](https://awesome-repositories.com/f/data-databases/distributed-analytical-runtimes/secure-distributed-analytics.md) — Executes analytical queries and data processing across clusters to keep sensitive information hidden from the processor. - [Joint Data Analysis](https://awesome-repositories.com/f/data-databases/joint-data-analysis.md) — Executes shared analytical workloads using cryptographic protocols across collaborative data resources. ([source](https://cdn.jsdelivr.net/gh/secretflow/secretflow@main/README.md)) ### DevOps & Infrastructure - [Decentralized Cluster Coordination](https://awesome-repositories.com/f/devops-infrastructure/decentralized-cluster-coordination.md) — Provides peer-to-peer coordination of compute tasks across multiple parties without a central authority. - [Privacy Orchestration](https://awesome-repositories.com/f/devops-infrastructure/distributed-orchestration/privacy-orchestration.md) — Provides coordination frameworks to manage privacy-preserving computation workloads across a cluster. ([source](https://cdn.jsdelivr.net/gh/secretflow/secretflow@main/README.md)) - [Distributed Task Schedulers](https://awesome-repositories.com/f/devops-infrastructure/distributed-task-schedulers.md) — Implements distributed task scheduling based on data locality and security constraints. ### Software Engineering & Architecture - [Distributed Cluster Coordination](https://awesome-repositories.com/f/software-engineering-architecture/distributed-cluster-coordination.md) — Coordinates secure workloads and cryptographic tasks across a cluster of independent nodes. - [Task Coordinations](https://awesome-repositories.com/f/software-engineering-architecture/distributed-coordination-systems/task-coordinations.md) — Synchronizes secure computation workflows and tracks task completion across distributed nodes. ([source](https://cdn.jsdelivr.net/gh/secretflow/secretflow@main/README.md))