# 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 →![Attachment.tiff](file:///Attachment.tiff)

  Learn how DATs evolve from registration to value realization.
* 🔹 Security & Privacy Model →![Attachment.tiff](file:///Attachment.tiff)

  Explore how encryption, TEE, and ZKP ensure data safety.
* 🔹 Developer Implementation →![Attachment.tiff](file:///Attachment.tiff)

  Start building and minting your first DAT.


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