OpenMined/PySyft

Features

• Remote Data Analysis - Executes machine learning workflows on datasets hosted on remote servers to maintain strict data privacy.
• Privacy-Preserving Machine Learning - Implements a framework for executing machine learning workflows on remote servers without accessing raw private data.
• Privacy-Preserving Compute Engines - Provides a remote computation engine that allows analysis jobs to run on private data sources without raw information leaving the host.
• Decentralized Analysis Orchestrators - Provides a decentralized orchestrator for coordinating remote analysis and synchronizing computation requests across offline or cloud-connected peers.
• Decentralized Data Analysis - Orchestrates the analysis of information spread across multiple offline or cloud-connected servers.
• Remote Function Execution - Sends computation logic to the data source, ensuring raw private data never leaves the host device.
• Privacy Orchestration - Coordinates remote data analysis and synchronizes computation requests across distributed peers while maintaining data privacy.
• Data and Resource Permissions - Provides granular controls for restricting visibility and operations on private datasets by requiring owner approval.
• Role-Based Access Control - Requires data owners to manually approve specific collaborators and computation actions through role-based permissions.
• Secure Data Collaboration - Manages permissions and approvals for multiple researchers to work on private datasets hosted by different owners.
• Privacy-Preserving Workflows - Implements a workflow cycle of using mock data for development and remote execution for production results.
• Secure Permission Management - Offers a platform for managing data permissions and authorizing collaborators to run computations on isolated private datasets.
• Mock Data Utilities - Provides synthetic data surrogates to validate analysis code locally before deploying to private remote servers.
• Code Execution Sandboxes - Runs remote computations within isolated virtual environments to prevent unauthorized access to the host system.
• Isolated Execution Sandboxes - Runs submitted computations in sandboxed virtual environments to restrict access to the underlying system.
• Federated Learning - Library for secure and private deep learning.
• General Machine Learning - Library for secure and private deep learning.
• Guardrails and AI Safety - Listed in the “Guardrails and AI Safety” section of the The Incredible Pytorch awesome list.
• Privacy and Safety - Library for secure, private deep learning using MPC.

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