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

1. Encrypt data → Generate hash
2. Upload to decentralized archive
3. Register metadata and hash on-chain
4. Validate via TEE or ZKP
5. Mint DAT token representing the asset
6. Use DAT to access AI services or earn rewards

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.