Architecture
The Data Anchoring Token (DAT) architecture defines the core technical stack that powers verifiable AI data ownership on the LazAI Network.
It provides a layered framework where data, metadata, and economic value interact securely through smart contracts and cryptographic proofs.
1. System Overview
At a high level, the DAT framework connects contributors, AI agents, and the blockchain using a multi-layered architecture:
graph TD
A[Contributor / AI Developer] --> B[Encryption & Data Layer]
B --> C[Metadata & Provenance Layer]
C --> D[Smart Contract Layer]
D --> E[Verification Layer (TEE / ZKP)]
E --> F[Tokenization & Economy Layer]
F --> G[DAT Holder / Service Consumer]This structure ensures every contributed dataset, model, or inference is:
Encrypted and verifiable
Anchored to an immutable provenance record
Represented as a semi-fungible DAT token
Linked to programmable usage and revenue logic
2. Layered Components
2.1 Encryption & Data Layer
Encrypts raw data locally before submission using hybrid AES + RSA.
Generates a unique data fingerprint (SHA-256 hash) for integrity tracking.
Stores encrypted payloads in decentralized archives (IPFS, Filecoin, or private storage).
Output: Encrypted file + integrity hash
2.2 Metadata & Provenance Layer
Records asset identity, class, description, and URI in a metadata schema.
Maintains the provenance of contribution (creator, timestamp, ownership chain).
Anchors metadata hashes to the blockchain for tamper-proof traceability.
Output: Immutable metadata anchor
2.3 Smart Contract Layer
Core on-chain logic that manages the DAT lifecycle:
Registering data contributions
Minting and binding tokens to assets
Managing value transfers and ownership rights
Enables composable operations like:
registerData(), mintDAT(), transferValue(), claimRewards()
Output: On-chain record of ownership, value, and access
2.4 Verification Layer
Validates submitted data through Trusted Execution Environments (TEE) or Zero-Knowledge Proofs (ZKPs).
Ensures the computation or dataset matches the registered proof without revealing the raw data.
Provides verifiable attestations used for DAT minting authorization.
Output: Signed proof of authenticity
2.5 Tokenization & Economy Layer
Issues a semi-fungible DAT token (SFT) representing the verified contribution.
Encodes three properties:
Ownership Certificate
Usage Rights (e.g., call credits, model usage)
Value Share (fractional rewards)
Integrates with payment and settlement contracts to automate royalty flow.
Output: Minted DAT with on-chain economic logic
3. Data Flow Summary
4. Smart Contract Structure
Function
Description
createClass(name, uri)
Defines a new class of AI assets (datasets, models, or agents).
mintDAT(owner, classId, value, shareRatio, expiry)
Issues a token for a verified contribution.
transferValue(fromToken, toToken, amount)
Enables fine-grained value or credit transfer.
claimRewards(classId)
Distributes on-chain rewards proportionally.
verifyData(hash, proof)
Validates integrity through off-chain verifier.
5. Integration Points
Integration
Description
TEE Verifiers
Used for confidential validation without exposing data.
AI Agents / Oracles
Consume DATs as compute or model credits.
External Data Feeds
Can be integrated via API or SDK for automated registration.
Wallets & Dashboards
Manage minting, ownership, and analytics visually.
6. Design Principles
Principle
Description
Privacy First
No unencrypted data leaves the contributor’s device.
Interoperability
Fully EVM-compatible and modular for AI agent extensions.
Composability
DATs can be split, merged, or reused across workflows.
Transparency
Each operation emits verifiable on-chain events.
7. Developer Navigation
🔹 Lifecycle & Value Semantics →
Learn how DATs evolve from registration to value realization.
🔹 Security & Privacy Model →
Explore how encryption, TEE, and ZKP ensure data safety.
🔹 Developer Implementation →
Start building and minting your first DAT.
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