This repository provides a collection of formal mathematical formulations and circuit designs for zero-knowledge (ZK) proofs applied to machine learning (ML) tasks. It aims to serve as a rigorous and collaborative reference for researchers and developers building privacy-preserving ML systems and verifiable computations using ZK circuits.

How It Works

The project focuses on translating ML operations into efficient ZK circuits by defining precise mathematical constraints over finite fields. Each blueprint offers formal descriptions, detailed constraints, and implementation guidelines, enabling the creation of verifiable computations that preserve data privacy. The approach emphasizes clarity and rigor, starting with fundamental operations like matrix addition and multiplication before progressing to more complex ML components.

Quick Start & Requirements

• Primary install / run command: No direct installation or execution commands are provided in the README. The repository contains blueprints and documentation.
• Non-default prerequisites and dependencies: None explicitly stated for viewing the blueprints.
• Estimated setup time or resource footprint: N/A for documentation-focused repository.
• Links: No direct links to quick-start guides, demos, or live examples are provided, though Jupyter notebooks are planned.

Highlighted Details

• Formal mathematical formulations for ZK circuit design.
• Well-documented circuit constraints for encoding computations in finite fields.
• Implementation guidelines for translating mathematical constructs into efficient circuits.
• Use cases in cryptographic protocols, zk-SNARKs, and privacy-preserving machine learning.

Maintenance & Community

• Contributions and discussions are welcomed.
• No specific community channels (Discord/Slack) or roadmap links are provided in the README.

Licensing & Compatibility

• The license is not specified in the provided README.
• Compatibility for commercial use or closed-source linking is undetermined.

Limitations & Caveats

The repository is described as "growing" with some planned features (e.g., pooling, more activation functions, Jupyter notebooks) marked as "coming soon" or "planned," indicating ongoing development and potential incompleteness.