Stage 2: Building

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Moksha is for Testing Purposes Only

Ensure you create a different configuration for data validation on testnet versus mainnet. Data privacy cannot be guaranteed in testnet.

Context

This guide walks you through a step-by-step process for deploying a new Data Liquidity Pool (DLP) on Vana. It covers smart contract setup, UI creation, proof-of-contribution implementation, and compliance with the latest VRC standards.

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Important Update:

Starting April 1, DLPs must comply with VRC-15 (Data Access), VRC-20 (DLP Token Standards), and VRC-14 (Rewards Eligibility) to qualify for incentives.

📚 Prerequisites

1. Smart Contract Knowledge: Familiarity with ERC-20, vesting, and smart contract deployment.
2. Understanding DataDAO Architecture: Review Vana Core Concepts
3. Node/Storage Infrastructure: Basic knowledge of IPFS, TEE (Trusted Execution Environment), and Docker.

🛠️ Step 1: Deploy the DLP Smart Contract

Every DLP is built on a smart contract that handles data rewards and proof-of-contribution logic.

🎯 Goal

• Deploy a compliant DLP Smart Contract and VRC-20-compliant token.

📦 Repo

• Use Vana’s audited smart contract templates from the GitHub repo here.
• Follow the deployment steps to create your smart contracts

✅ Key Actions

• Deploy an ERC-20 token with a fixed or predictable supply.
• Implement vesting with a minimum 6-month linear unlock for team allocation. You can use OpenZeppelin contract for this.
• Add 48-hour timelocks for any major upgrade changes.
• Verify smart contract on Vanascan.

Checklist:

• Token has predictable supply with vesting.
• Contract verified on explorer.
• No unlimited minting or large rebase logic.

🖥️ Step 2: Build a UI for Data Contributors

Your DLP needs a user interface where contributors can upload, encrypt, and validate their data.

🎯 Goal

• Build a user-friendly interface where contributors can manage and submit data.
• Ensure encryption and access control mechanisms are integrated.

📦 UI Repo

• Use the open-source DataDAO UI template here.
• Customize the UI based on your data contributor requirements.

✅ Key Actions

• Integrate client-side encryption to protect user data.
• Educate contributors about data sources (e.g., GDPR/CCPA data exports).

Tip: Some DLPs skip UI and use scrapers or Chrome extensions to gather data.

🔎 Step 3: Implement Proof-of-Contribution

A critical piece of your DLP is the proof-of-contribution (PoC) function that validates data quality.

🎯 Goal

• Implement and refine PoC logic to validate and reward data contributions.
• Align PoC with VRC-5, ensuring meaningful and valid data.

📦 PoC Template

• Use the Satya PoC template as a starting point.
• Customize the PoC pipeline to match your DLP’s requirements.

✅ Key Actions

• Define refinement logic for data cleansing and anonymization.
• Validate data based on meaningfulness, validity, and authenticity.
• Publish attestation schemas onchain.

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Example:

See the ChatGPT DataDAO’s PoC example.

📡 Step 4: Refine Your Data Contributions

Refinement is mandatory under VRC-15. You need to structure and encrypt user data before storing it.

🎯 Goal

• Add a refinement step to transform raw data into a structured format.
• Encrypt data with a DLP-specific public key.

📦 Refinement Repo

• Use the refinement template here.

🔗 Requirements

• Meaningfulness: The data should contribute novel and valuable insights to the DataDAO's overall dataset. It should enhance the richness and diversity of the available information.
• Validity: The data must adhere to a schema that is consistent with the existing data within the DataDAO. Conforming to a standardized structure ensures compatibility and facilitates efficient data processing.
• Authenticity and Contributor's rights: The data must be genuine and, if required, originate from a legitimate account. This ensures the integrity and reliability of the information. The data contributor submitting the data must have the necessary rights and permissions to do so.

✅ Key Actions

1. Extend Your PoC Pipeline
   • Add refinement as a Docker process.
   • Output encrypted libSQL files.
2. Handle Storage
   • Pin refined data on IPFS using providers like Pinata or Infura.
3. Publish Refiner & Schema Onchain
   • Add schema structure to IPFS.
   • Point the DataRefinerRegistry to the Docker URL with your refinement.

🔗 Step 5: Register Your DLP

To qualify for rewards, DLPs must be registered with the Vana Network.

🎯 Goal

• Register your DLP with the DLPRootContract and meet minimum staking thresholds.
• Ensure compliance with VRC-14 and VRC-5 for rewards eligibility.

✅ Key Actions

1. Data Registry Integration
   • Attach refined data IPFS CIDs to the Data Registry.
   • Publish onchain permissions for query access by data consumers
2. Grant Query Access & Define Pricing
   • Define access rules and pricing for data consumers.
   • Publish pricing via a website, email, or Discord.
3. Test Query Access
   • Run test queries in a TEE environment to confirm data integrity.

🧩 Step 6: Launch a VRC-20 Compliant DLP Token

To receive rewards, you need a DLP token that meets VRC-20 standards.

🎯 Goal

• Launch a DLP token or upgrade an existing one.
• Ensure compliance with supply, vesting, and security rules.

✅ Key Actions

1. Define Token Supply & Emissions
   • Fixed cap or public emission schedule.
   • Minimum 6-month vesting for team allocations.
2. Implement Key Security Requirements
   • 48-hour timelocks for fee changes or contract upgrades.
   • Cap transfer fees at ≤3%.
3. Document Token Utility
   • Explain how the token interacts with data usage.

⚡️ Step 7: Connect to Satya for Proof Validation (Optional)

For advanced validation, connect to Satya’s decentralized network to verify proof-of-contribution.

🎯 Goal

• Enable decentralized proof validation to boost security.
• Leverage Satya for enhanced validator redundancy.

✅ Key Actions

• Follow the Satya Integration Guide.
• Optionally implement client-side ZK Proofs or run the PoC function on your infrastructure.