The main goal of this mentorship project, Fabric Private Chaincode and CC-Tools for privacy-sensitive applications, was to explore how a confidential digital asset system with programmable escrow can be built on Hyperledger Fabric, without exposing sensitive transaction details.
Specifically, I wanted to demonstrate that digital assets can be transferred and locked into escrow while preserving confidentiality by using Fabric Private Chaincode (FPC) and accelerating development with CC-Tools on the Hyperledger Fabric platform.
In this project, a digital asset is transferred into an escrow whose logic executes inside a secure enclave (Intel SGX). This design protects both the confidentiality of transaction data and the integrity of the escrow business logic, even in severe threat scenarios where a blockchain peer may be compromised.
On a personal level, I was aiming to gain hands-on experience with Hyperledger Fabric, Fabric Private Chaincode, and CC-Tools by implementing a realistic, end-to-end prototype rather than a purely conceptual demo.
My goals for this project were to:
One of the most important lessons from this project was that designing the protocol was significantly more challenging than implementing the code.
At a high level, I started with the following requirements:
With Fabric Private Chaincode, the escrow smart contract runs inside an Intel SGX enclave, which protects both the code and the data. Many escrow designs rely on time-based conditions; however, Intel SGX does not provide access to trusted time, which made time-based escrows impractical.
To overcome this limitation, I designed a secret-based escrow using a hash-lock mechanism. The escrowed funds are released only when a valid secret and signature are presented, with all verification performed securely inside the enclave. This approach avoids the need for trusted time while maintaining strong security guarantees.
Another challenge was data modeling. In Hyperledger Fabric, application state is stored as key-value pairs, which means that complex data structures must be carefully decomposed. CC-Tools significantly simplified this process by abstracting away low-level interactions with the key-value store, allowing me to focus on business logic instead of storage mechanics.
While testing user authentication and authorization, I encountered debugging challenges related to Fabric’s identity and permission model. Understanding how identities, certificates, and access control interact in a permissioned blockchain took time. Fabric’s documentation and improved debugging tools were instrumental in resolving these issues.
Finally, maintaining simplicity while preserving security was one of the most difficult aspects of the project. Avoiding over-engineering required multiple design iterations and many discussions with my mentors. Their guidance, along with established best practices for using Fabric Private Chaincode and CC-Tools, helped refine the system into its final form.
I successfully completed the design and implementation of a confidential digital asset escrow application for Hyperledger Fabric using Fabric Private Chaincode and CC-Tools. The source code is available in the Fabric Private Chaincode repository under the samples section.
The implementation demonstrates how confidential digital asset custody and escrow logic can be realized using confidential computing, specifically Intel SGX. Key features include:
A demo video below accompanies the project and showcases end-to-end workflows such as:
The completed system supports:
By leveraging CC-Tools, the implementation of wallet and asset data structures is greatly simplified, allowing development to focus on business logic rather than low-level key-value store interactions.
By using Fabric Private Chaincode, only encrypted data is ever stored on the ledger, while all transaction processing is executed in cleartext inside an Intel SGX enclave. Executing the escrow smart contract within the enclave provides strong guarantees for confidentiality and integrity, including secure balance computation and secret and signature verification.
This project highlighted that design is often harder than coding. While writing code can be iterative, making sound architectural decisions early on is critical for building secure and maintainable systems.
Keeping the solution simple proved essential. Avoiding over-complication helped improve clarity, reduce security risks, and make the system easier to maintain.
I also gained hands-on experience with Hyperledger Fabric, Fabric Private Chaincode, and CC-Tools, developing a deeper understanding of how these components work together to enable confidential and secure blockchain applications.
Finally, the project helped improve my Go (Golang) development practices, including better code structure, clearer separation of concerns, and the use of automation to streamline development.
In closing, I would like to thank my mentors for their continuous guidance and valuable feedback throughout this project. Their support and design insights were essential in navigating the technical challenges and refining the final solution. The LFDT mentorship program was a great experience, and I had a lot of fun working on this project while contributing to the LFDT community.