syft-client 0.1.113

A simple client library for setting up secure communication channels using Google Drive

Syft-client

Syft client 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

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="[e-mail]")
ds = sc.login_ds(email="[e-mail]")

# Peer request & approve
ds.add_peer("[e-mail]")
do.approve_peer_request("[e-mail]")

# Create & sync dataset
do.create_dataset(
    name="census",
    mock_path="mock.txt",
    private_path="private.txt",
    users=["[e-mail]"],
)
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="[e-mail]",
    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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