Summary

OpenPCC is an open-source framework enabling provably private AI inference, inspired by Apple's Private Cloud Compute. It allows users to run AI models on their own infrastructure without exposing sensitive data like prompts, outputs, or logs, enforcing privacy through encrypted streaming, hardware attestation, and unlinkable requests. The project aims to establish a transparent, community-governed standard for AI data privacy.

How It Works

The framework's core approach leverages encrypted streaming, hardware attestation, and unlinkable requests to ensure data confidentiality during AI inference. This design facilitates auditable and deployable AI solutions on custom infrastructure, guaranteeing that user prompts, model outputs, and operational logs remain private.

Quick Start & Requirements

Development commands are managed via go tool mage. Mage can be installed with go install github.com/magefile/mage@latest. For local development, run in-memory services with mage runMemServices and test client requests using mage runClient. The compute node implementation is located in a separate repository: https://github.com/confidentsecurity/confidentcompute. A Go client example demonstrates configuration with API keys and transparency verifiers. Primary dependencies include the Go toolchain.

Highlighted Details

• Offers "provably private AI inference" capabilities.
• An open-source, auditable alternative inspired by Apple's Private Cloud Compute.
• Employs encrypted streaming, hardware attestation, and unlinkable requests for robust data privacy.
• Designed for deployment on user-controlled infrastructure.

Maintenance & Community

Confident Security is developing a managed service, CONFSEC, based on the OpenPCC standard, accessible at https://confident.security. The project's whitepaper is available at https://github.com/openpcc/openpcc/blob/main/whitepaper/openpcc.pdf. No other community channels or contributor details were specified in the provided text.

Licensing & Compatibility

Licensing information is not provided in the README snippet.

Limitations & Caveats

The compute node implementation is maintained in a separate repository, indicating this project focuses on the client/framework aspect. Development examples primarily cover in-memory services and Go client usage, with limited detail on production deployment specifics within this snippet.