Abstract

Fabric Private Chaincode (FPC) uses Confidential Computing technology like Intel SGX to protect chaincode and data during execution on endorsing peers. Client applications interact with private chaincode via the FPC Client SDK, which encrypts and authenticates invocation arguments before sending them to endorsing peers. Chaincodes can be developed in C++ or Golang.

The Hyperledger Labs CC-Tools library simplifies learning, developing, and deploying Hyperledger Fabric chaincode in Golang.

Our project aims to design and integrate FPC as a target for code developed using CC-Tools, including creating samples and documentation.

Official Repository: https://github.com/hyperledger-labs/aifaq

Mentor and Mentee

Mentors: Barbara (Bobbi) Muscara, Gianluca Capuzzi, Tripur Joshi, Swapnil Tripathi, Arunima Chaudhuri

Mentee: Xitong(Jacqueline) Zhang, Sarvesh Atawane, Sauradip Ghosh, Peter Atef

Deliverables

An LLM prototype with acceptable time response and implementation costs
A Front-end component prototype
A simple container architecture
A good quality documentation

Milestones

Eval 1:

ChatBot Front-end Component

Eval 2:

Container Architecture

Eval 3:

Integration & Testing: version installed on Cloud

Eval 4:

All deliverables (documentation, code, performance report, cost evaluation)

Timeline

June 3 - June 23	Onboarding Understand the project scope and learn about LLM (Large Language Models) review the existing Hyperledger Labs AIFAQ documentation
June 24 - July 5	Understand the project scope and learn about LLM (Large Language Models) research and document the basics of LLM, focusing on how they can be applied to create intelligent ChatBots setup and familiarize with existing codespace
July 8 - July 19	Develop the front-end component of the ChatBot design a user-friendly interface for the ChatBot using JavaScript implement the interface as a functional prototype that can later be integrated with the backend LLM
July 22 - July 26	1st Quarter Evaluation
July 29 - August 9	Understand Cloud Architecture and Deployment research cloud service providers (e.g., AWS, Google Cloud, Azure) and their offerings document the pros and cons of each service with respect to the project's needs and select one for deployment. begin the deployment of the front-end component as a test.
August 12 - August 23	Containerization research and document the basics of container technology (e.g., Docker). create a simple container for the ChatBot's front-end.
August 26 - August 31	Buffer Time & Documentation catch up on documenting the current progress
September 2 - September 6	Midterm Evaluation
September 9 - September 20	Integration & Testing integrate the front-end component with the LLM backend, ensuring they communicate effectively. conduct initial testing and document any issues or bugs.
September 23 - October 4	Cloud Server Deployment finalize the deployment of the ChatBot on the chosen cloud service perform comprehensive testing to ensure functionality and performance standards are met
October 7 - October 11	Buffer Time & Documentation
October 14 - October 18	3rd Quarter Evaluation
October 21 - November 1	Project Documentation & Quality Assurance document the project extensively, including setup instructions, user guides, and technical details perform final rounds of testing, focusing on user experience and bug fixing
November 4 - November 8	Buffer Time
November 11 - November 29	Project Wrap-up, Review & Feedback organize a project demonstration for stakeholders to gather feedback reflect on the project process, documenting lessons learned and potential improvements finalize all project documentation and ensure all code and resources are well-organized and accessible outline potential future enhancements and areas for further development Final Evaluation

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Project Plan - Hyperledger Fabric CC-Tools Support for Fabric Private Chaincode