PySyft is a privacy-preserving data science platform that enables data owners to share private datasets and approve code execution without exposing raw data. The codebase is a monorepo with multiple interdependent packages: `syft` (sync engine, peer management, encryption), `syft-rds` (high-level remote data science client for datasets and jobs), `syft-job` (job lifecycle, runner, disclosures), `syft-datasets` (dataset storage with versioned protocol codecs), `syft-enclave` (confidential computing with TEE attestation via Confidential Spaces or Tinfoil, signed receipts), `syft-bg` (background services for auto-approval, email notifications, systemd management, TUI), `syft-permissions` (declarative ACL engine), `syft-restrict` (static code verification and obfuscation for private regions), `syft-migration` (generic versioned migration framework for objects), `syft-notebook-ui` (rich HTML representation for Jupyter), and `syft-perms` (file-level permission browser). The core workflow: data scientists submit Python jobs encapsulating analysis code → data owners approve (manually or automatically) → jobs run in sandboxed environments or TEE enclaves → results and logs are staged and released according to multi-party disclosure policies. The system uses Google Drive as the default sync transport with offline-first P2P file exchange, protocol versioning for backward compatibility, and cryptographic key pinning for trust.
This repository is not a software application, service, or data pipeline; it is a curated awesome-list of open-source multiparty computation (MPC) resources, stored almost entirely as a single Markdown document plus contribution and code-of-conduct files. The core artifact, readme.md, categorizes roughly 100 projects and educational materials into Theory (books, courses, tutorials), Software (apps, frameworks, primitives, protocols, tools, retired software), and Workshops. Each entry includes a concise description, links to relevant academic papers, and maintenance context. There is no executable code, no package manifest, no CI configuration, and no tests; the repository's real architecture is its taxonomy and the editorial rules in contributing.md.