Zus Data Ledger

Decentralized Contribution Accounting & Verification Layer

1. Overview

Zus Data Ledger (ZDL) is the cryptographic accounting and verification layer of the Zusama ecosystem.

It functions as a hybrid on-chain/off-chain data integrity framework designed to:

• Record node contribution metadata

• Validate AI training participation

• Calculate reward allocation weights

• Track NFT multipliers and revenue share

• Ensure transparency in economic distribution

ZDL acts as the trust layer between decentralized compute nodes, AI training processes, and on-chain reward settlement.

It does not store raw user data. It stores proof, contribution weights, and verifiable state transitions.

2. Architectural Design

Zus Data Ledger operates under a dual-layer architecture:

2.1 Off-Chain High-Frequency Ledger Layer

Purpose:

• Handle high-throughput contribution logging

• Store encrypted gradient metadata

• Track performance metrics in real time

Characteristics:

• Distributed logging clusters

• Append-only event streams

• Merkle-tree structured batching

• Optimized for scalability and low latency

This layer processes:

• Node uptime events

• Training batch completion

• Telegram interaction signals

• Extension activity checkpoints

• Contribution score recalculations

To prevent centralization risk, logs are periodically hashed and anchored on-chain.

2.2 On-Chain Settlement Layer (Solana)

The on-chain layer acts as the final settlement and proof anchor.

Functions:

• Record Proof-of-Training hashes

• Validate NFT ownership state

• Confirm reward distribution transactions

• Store epoch-based contribution summaries

• Trigger smart-contract reward allocation

Each settlement cycle produces:

• Epoch Contribution Root Hash

• Reward Allocation Mapping

• Token Distribution Execution

Solana is used due to:

• High throughput

• Low transaction cost

• Fast finality

• NFT-native infrastructure

3. Contribution Recording Model

Zus Data Ledger records contribution through a structured event schema.

3.1 Contribution Event Object

Each node submission generates a structured record:

{
  Node_ID,
  Wallet_Address,
  Node_Type,
  Task_ID,
  Contribution_Type,
  Gradient_Hash,
  Compute_Weight,
  Data_Weight,
  Social_Weight,
  Timestamp,
  Reputation_Score
}

Contribution types include:

• Compute Training Contribution

• Behavioral Data Contribution

• Telegram Social Interaction Contribution

• Validation Contribution

• NFT Multiplier Application

Raw gradients are never stored — only hashed references.

4. Epoch-Based Accounting

Zusama operates in Reward Epochs (e.g., hourly or daily cycles).

Each epoch:

1. Aggregates all contribution events

2. Calculates weighted contribution scores

3. Applies NFT multipliers

4. Validates node reputation

5. Generates Merkle Root

6. Anchors root hash on-chain

7. Distributes rewards

This ensures:

• Deterministic reward computation

• Verifiable distribution history

• Auditability

5. Contribution Scoring Engine

ZDL integrates a deterministic scoring engine:

5.1 Base Formula

Total Score = 
(Compute_Weight × α) +
(Data_Weight × β) +
(Social_Weight × γ)

Where:

• α = Compute priority coefficient

• β = Data quality coefficient

• γ = Social engagement coefficient

The result is then multiplied by:

• Uptime Reliability Factor

• Reputation Index

• NFT Multiplier

Final Reward Share:

Node Reward = 
(Total Score / Global Epoch Score) × Reward Pool

---

6. NFT Multiplier Tracking

ZDL maintains NFT state indexing:

• NFT ID

• Rarity tier (D, C, B, A, S)

• Bonus attributes

• Stacking configuration

If a user holds multiple NFTs:

• Bonus attributes are aggregated

• Multipliers are applied cumulatively

• Stacking caps (if defined by governance) are enforced

NFT metadata references are validated on-chain before multiplier activation.

7. Data Integrity & Proof-of-Training

ZDL integrates cryptographic validation mechanisms:

7.1 Gradient Hashing

Each local training update produces:

• Gradient hash

• Model version reference

• Training batch signature

7.2 Secure Aggregation Verification

Before reward eligibility:

• Gradient consistency check

• Statistical outlier detection

• Model impact scoring

Low-impact or malicious updates:

• Receive reduced weighting

• Trigger reputation penalty

8. Reputation & Slashing Logic

Each node maintains a dynamic Reputation Score (RS):

Factors:

• Historical accuracy

• Validation success rate

• Uptime reliability

• Malicious behavior detection

Nodes may face:

• Reward reduction

• Temporary suspension

• Multiplier loss

• Slashing (future governance mechanism)

This prevents Sybil attacks and gradient poisoning.

9. Cross-Platform Identity Synchronization

Zus Data Ledger supports unified identity across:

• Browser Extension

• Desktop Node

• VPS Node

• Telegram Node

• Super Node NFT

All activity maps to:

Wallet_Address → Unified Contribution Profile

This allows:

• Aggregated scoring

• Cross-platform reward stacking

• Unified NFT multiplier application

10. Transparency & Auditability

ZDL provides:

• Public epoch hash verification

• Reward calculation transparency

• Deterministic distribution logic

• On-chain proof anchoring

Developers and third parties can verify:

• Reward fairness

• Contribution accuracy

• NFT multiplier validity

Future upgrades may include:

• Zero-Knowledge Proof validation

• Public Merkle proof explorer

• AI training impact analytics dashboard

11. Security Considerations

ZDL mitigates risks including:

• Gradient poisoning

• Replay attacks

• Timestamp manipulation

• Fake node duplication

• Sybil multi-account farming

Mitigation layers include:

• Wallet-based identity

• NFT-based economic barrier

• Rate limiting

• Encrypted transmission

• Reputation decay system