DEEPPOWERS is a Fully Homomorphic Encryption (FHE) framework designed to provide end-to-end privacy for the Model Context Protocol (MCP) ecosystem. It enables secure, efficient computation on encrypted data for LLM interactions, benefiting developers and organizations prioritizing data privacy in AI workflows.

How It Works

DEEPPOWERS integrates FHE with the MCP protocol, allowing computations on encrypted data without decryption. It leverages Concrete-ML for privacy-preserving machine learning, extending its capabilities to LLM interactions. The architecture involves MCP clients for local encryption/decryption, MCP servers for task management, TEE Compute Nodes for secure computation within Trusted Execution Environments, Decentralised Review Nodes for consensus, and a Blockchain for immutable audit logs.

Quick Start & Requirements

• Demo: https://github.com/deeppowers/deeppowers/blob/main/docs/demo.md
• Dependencies: C++, Python, CUDA. Specific FHE libraries (Microsoft SEAL, OpenFHE, IBM HELib) are utilized. Hardware acceleration is under development.

Highlighted Details

• End-to-end encryption for user-LLM interactions.
• Seamless integration with MCP servers and LLMs (DeepSeek, GPT, Gemini, Claude).
• Combines FHE with TEE and Blockchain for enhanced security and traceability.
• Hardware optimizations for Concrete-ML are in development for accelerated encrypted computation.

Maintenance & Community

• Actively under development with a roadmap outlining phases for core infrastructure, feature expansion, and enterprise integration.
• Plans include a native token for incentives, governance, and payments.
• Contact: FHEDEEP CA：6uHQEuSFwrCYKByHfkVgWrGWTESRZpNm951PaqxWpump

Licensing & Compatibility

• License details are not explicitly stated in the README.

Limitations & Caveats

• FHE computation has significant performance overhead, requiring algorithm adaptation and optimization.
• Key management and recovery are critical user responsibilities.
• FHE has limited support for certain non-linear operations, necessitating FHE-friendly algorithms or homomorphic approximation.
• The SDK is currently under active development, with advanced features and optimizations planned for future phases.