Detailed Introduction
OpenPCC is an open-source framework for verifiably private inference, designed to enable auditable model inference while preserving data privacy. Maintained by the community, the project focuses on deploying inference services in environments with strict compliance or privacy requirements. By combining cryptographic techniques, verifiable computation, and verifiable execution paths, OpenPCC reduces the trust required between model providers and data owners when performing inference tasks.
Main Features
- Verifiable inference workflows that facilitate auditing and compliance.
- Privacy-preserving design to minimize exposure of plaintext data between parties.
- Production-oriented framework components for integration with common inference infrastructures.
- Open-source implementation for reproducibility, auditing, and customization.
Use Cases
- Model inference in privacy-sensitive industries such as healthcare and finance.
- Providing verifiable collaboration patterns between third-party data providers and model vendors.
- Serving as a research and engineering platform to validate privacy-enhancing inference methods.
Technical Features
- Architecture combining verifiable computation and cryptographic primitives to reduce plaintext exposure.
- Pluggable components for integration with existing inference services and pipelines.
- Focus on auditability and verifiability, supporting generation of proof artifacts for audits.
- Community-driven implementation to improve security auditing and transparency.