PySyft v2

PySyft lets data scientists submit computations which are ran by data owners on private data — all through cloud storage their organizations already use (Google Drive, Microsoft 365, etc.). No new infrastructure required.

Docs

• Workflow — End-to-end privacy-preserving data analysis workflow
• API Reference — All public client methods and properties
• Authentication & Setup — Google Cloud OAuth setup for local/Jupyter usage
• Background Services — Email notifications, auto-approval, and TUI dashboard
• Connections — How the Google Drive transport layer works
• Permissions - Permissions for syft-client
• Enclaves - Enclaves with syft-client

Features

• Privacy-preserving — Private data never leaves the data owner's machine; only approved results are shared
• Transport-agnostic — Works over Google Drive today, extensible to any file-based transport
• Offline-first — Full functionality even when peers are offline; changes sync when connectivity resumes
• Peer-to-peer with explicit auth — Data owners must approve each collaborator before any data flows
• Isolated job execution — Jobs run in sandboxed Python virtual environments with controlled access to private data
• Dataset sharing with mock/private separation — Data scientists explore mock data, then submit jobs that run on the real thing

Quick Start

uv pip install syft-client

import syft_client as sc

# Login (colab auth, for non-colab pass token_path)
do = sc.login_do(email="do@org.com")
ds = sc.login_ds(email="ds@org.com")

# Peer request & approve
ds.add_peer("do@org.com")
do.approve_peer_request("ds@org.com")

# Create & sync dataset
do.create_dataset(
    name="census",
    mock_path="mock.txt",
    private_path="private.txt",
    users=["ds@org.com"],
)
do.sync(); ds.sync()
datasets = ds.datasets.get_all()

Write an analysis.py that reads the dataset and produces a result in our case this is just the length of the data. Inside a job, resolve_dataset_file_path automatically resolves to the private data:

# analysis.py
import json
import syft_client as sc

data_path = sc.resolve_dataset_file_path("census")
with open(data_path, "r") as f:
    data = f.read()

with open("outputs/result.json", "w") as f:
    json.dump({"length": len(data)}, f)

Submit the job and retrieve results:

# Submit job
ds.submit_python_job(
    user="do@org.com",
    code_path="analysis.py",
)
ds.sync(); do.sync()

# Data owner Approves & runs job
do.jobs[0].approve()
do.process_approved_jobs(share_outputs_with_submitter=True)
do.sync(); ds.sync()
result = open(ds.jobs[-1].output_paths[0]).read()

Packages

Package	Description
syft-datasets	Dataset management and sharing
syft-job	Job submission and execution
syft-permissions	Permission system for Syft datasites
syft-perms	User-facing permission API for Syft datasites
syft-bg	Background services TUI dashboard for SyftBox
syft-notebook-ui	Jupyter notebook display utilities

Development

# Install in development mode
uv pip install -e .

# Run tests
just test-unit          # Unit tests (fast, mocked)
just test-integration   # Integration tests (slow, real API)

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Built by OpenMined — building open-source technology for privacy-preserving data science and AI.

Support

For questions about PySyft, reach out via #support on Slack.

Community

Supported by the OpenMined Foundation, the OpenMined Community is an online network of over 17,000 technologists, researchers, and industry professionals keen to unlock 1000x more data in every scientific field and industry.

Contributors

OpenMined and Syft appreciates all contributors, if you would like to fix a bug or suggest a new feature, please reach out via Github or Slack!

About OpenMined

OpenMined is a non-profit foundation creating technology infrastructure that helps researchers get answers from data without needing a copy or direct access. Our community of technologists is building Syft.

Supporters

License

Apache License 2.0
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