OpenMined/PySyft

Perform data science on data that remains in someone else's server

What it solves

PySyft allows data scientists to perform analysis and run computations on private data without the data ever leaving the owner's machine. It solves the problem of accessing sensitive data for AI and research by ensuring that only approved results are shared, rather than the raw data itself.

How it works

The system uses a peer-to-peer architecture where data owners and data scientists collaborate. Data scientists submit Python jobs (code) to data owners. These jobs are executed in isolated, sandboxed virtual environments on the owner's machine. The process is transport-agnostic, utilizing existing cloud storage (like Google Drive or Microsoft 365) as a sync engine to exchange requests and results.

Who it’s for

It is designed for data scientists and researchers who need to obtain insights from private datasets and data owners (organizations or individuals) who want to maintain strict control over their sensitive information.

Highlights

  • Privacy-preserving: Raw private data remains on the owner's machine.
  • Transport-agnostic: Works over existing file-based cloud storage providers.
  • Offline-first: Syncs changes when connectivity resumes, allowing functionality while peers are offline.
  • Isolated Execution: Jobs run in sandboxed Python environments to protect the owner's machine.
  • Mock Data Support: Data scientists can explore and develop their code using mock data before submitting jobs to run on real private data.

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