🔍Detailed Design of DAT

The DAT protocol introduces a structured AI asset management system, leveraging on-chain metadata, off-chain data storage, and cryptographic proofs to ensure security, transparency, and usability.

Core Data Structure of DAT

Each DAT token instance consists of the following attributes:

Attribute	Type	Description
DAT ID	uint256	Unique identifier for the tokenized AI dataset or model.
Asset Type	enum { Dataset, Model, AI-Agent }	Specifies whether the token represents a dataset, AI model, or an autonomous AI agent.
Slot ID (Category Tagging)	uint256	Defines the classification of AI assets, allowing datasets/models of the same category to be grouped for composability.
Partitioned Value (Token Units)	uint256	Represents divisible ownership or access quotas, enabling fractional AI asset ownership and licensing.
Access Control (Permissions)	struct { readOnly, trainable, inferenceOnly, composable }	Defines who can use the AI dataset/model and under what conditions.
Dataset Provenance Hash	bytes32	Hash of the dataset, anchoring off-chain data integrity (IPFS, Arweave, Filecoin).
Usage State	mapping (address => uint256)	Tracks AI training/inference requests, ensuring proper usage-based payments.
Revenue Model	mapping (address => uint256)	Specifies how revenue is distributed among data contributors, model developers, and validators.

AI Data Anchoring & Validation Mechanism

The integrity and trustworthiness of AI datasets and models are secured through on-chain anchoring and decentralized verification mechanisms.

• Data Anchoring Process

  • Dataset Submission → iDAO submits dataset metadata, cryptographic proofs (Merkle Tree, ZK Proofs), and storage references (IPFS, Arweave).

  • Anchoring & Hashing → A dataset fingerprint (hash) is generated and stored on-chain via DAT token metadata.

  • Token Minting → The system mints a DAT token representing the dataset, with predefined access rights, licensing conditions, and revenue-sharing logic.

• Decentralized Verification

  • Proof-of-Authenticity: AI datasets and models require zero-knowledge proof (ZKP) verification, ensuring they were generated from valid sources.

  • Fraud-Proof Mechanism: Challengers can submit fraud proofs against datasets/models that violate integrity rules. If successfully disputed, the original submitter is slashed, and the challenger is rewarded.