LP-102: Immutable Training Ledger for Privacy-Preserving AI

Abstract

This proposal establishes an immutable, on-chain ledger system for recording all AI model training data, user interactions, and per-user fine-tuning operations. Every user interaction creates a personalized model fork, with all training data and priors recorded immutably on-chain. This creates a transparent, auditable, and privacy-preserving foundation for next-generation open AI where users own their personalized models.

Motivation

Current AI systems have fundamental flaws:

No Per-User Personalization: Generic models for all users
Opaque Training: Users don't know what data trained their models
No Ownership: Users can't own or control their personalized models
Privacy Violations: User interactions harvested without consent
No Portability: Personalization locked to platforms
No Attribution: Training data sources not credited

This proposal solves these by creating per-user model forks with immutable training history.

Specification

Per-User Model Architecture

pub struct UserModel {
    // Unique model ID per user
    pub model_id: ModelId,
    pub user_id: UserId,
    
    // Fork from base model
    pub base_model: ModelHash,
    pub fork_height: u64,
    
    // Personal training chain
    pub training_chain: Vec<TrainingOp>,
    
    // Current model state (encrypted)
    pub model_state: EncryptedModelState,
    
    // Accumulated priors
    pub priors: PersonalPriors,
}

pub struct TrainingOp {
    // Each interaction is a training operation
    pub op_id: OperationId,
    pub timestamp: u64,
    
    // User interaction data
    pub input: EncryptedInput,
    pub output: EncryptedOutput,
    pub feedback: Option<UserFeedback>,
    
    // Model delta from this interaction
    pub gradient: EncryptedGradient,
    
    // Updated priors
    pub prior_update: PriorDelta,
}

Immutable Training Ledger

pub struct TrainingLedger {
    // Global chain of all training
    pub global_chain: Vec<TrainingBlock>,
    
    // Per-user model chains
    pub user_chains: HashMap<UserId, UserModelChain>,
    
    // Model evolution tree
    pub model_tree: ModelEvolutionTree,
    
    // Privacy proofs
    pub privacy_proofs: Vec<ZKProof>,
}

pub struct TrainingBlock {
    pub block_number: u64,
    pub timestamp: u64,
    pub merkle_root: Hash,
    
    // All user training ops in this block
    pub training_ops: Vec<UserTrainingOp>,
    
    // Aggregated model updates (privacy-preserving)
    pub aggregated_update: HomomorphicSum,
    
    // Block signature (post-quantum)
    pub signature: MLDSASignature,
}

Privacy-Preserving Training

Zero-Knowledge Training Proofs

impl UserModel {
    // Prove training without revealing data
    pub fn generate_training_proof(
        &self,
        interaction: &Interaction
    ) -> ZKTrainingProof {
        ZKTrainingProof {
            // Prove interaction occurred
            interaction_proof: prove_interaction(interaction),
            
            // Prove model improved
            improvement_proof: prove_improvement(
                self.model_state,
                interaction.gradient
            ),
            
            // Prove data ownership
            ownership_proof: prove_ownership(
                self.user_id,
                interaction
            ),
        }
    }
}

Homomorphic Aggregation

pub fn aggregate_user_updates(
    updates: Vec<EncryptedGradient>
) -> AggregatedUpdate {
    // Sum gradients without decryption
    let encrypted_sum = homomorphic_sum(updates);
    
    // Generate proof of correct aggregation
    let proof = generate_aggregation_proof(&updates, &encrypted_sum);
    
    AggregatedUpdate {
        sum: encrypted_sum,
        proof,
        contributor_count: updates.len(),
    }
}

Smart Contract Interface

interface IUserModelLedger {
    // Create personal model fork
    function forkModel(
        bytes32 baseModel,
        bytes calldata zkProof
    ) external returns (bytes32 userModelId);
    
    // Record training interaction
    function recordTraining(
        bytes32 userModelId,
        bytes calldata encryptedInteraction,
        bytes calldata trainingProof
    ) external returns (uint256 opId);
    
    // Query user's training history
    function getUserHistory(
        address user,
        bytes calldata authProof
    ) external view returns (TrainingOp[] memory);
    
    // Export user model (for portability)
    function exportModel(
        bytes32 userModelId,
        bytes calldata ownershipProof
    ) external returns (bytes memory encryptedModel);
    
    // Monetize training contributions
    function claimTrainingRewards(
        bytes32 userModelId,
        uint256[] calldata opIds
    ) external returns (uint256 rewards);
}

Model Evolution and Priors

pub struct ModelEvolution {
    // Each user's model evolves independently
    pub user_evolution: HashMap<UserId, Evolution>,
    
    // Global model learns from all users
    pub global_evolution: GlobalEvolution,
    
    // Priors passed to next generation
    pub generation_priors: GenerationPriors,
}

pub struct Evolution {
    pub generations: Vec<Generation>,
    pub fitness_history: Vec<FitnessScore>,
    pub adaptation_rate: f64,
}

pub struct GenerationPriors {
    // What this generation learned
    pub knowledge: EncryptedKnowledge,
    
    // User preference patterns
    pub preferences: AggregatedPreferences,
    
    // Safety boundaries discovered
    pub safety_constraints: SafetyConstraints,
    
    // Performance baselines
    pub baselines: PerformanceMetrics,
}

Cross-Chain Synchronization

message UserModelSync {
    // User model state
    UserModelState state = 1;
    
    // Training operations to sync
    repeated TrainingOp ops = 2;
    
    // Cross-chain proof
    CrossChainProof proof = 3;
    
    // Source and target chains
    string source_chain = 4;
    string target_chain = 5;
}

Implementation

Phase 1: Foundation (Q1 2025)

Basic per-user model forking
Simple training recording
Initial privacy features

Phase 2: Privacy Layer (Q2 2025)

Zero-knowledge proofs
Homomorphic aggregation
Encrypted model states

Phase 3: Evolution (Q3 2025)

Model evolution tracking
Prior accumulation
Generation advancement

Phase 4: Monetization (Q4 2025)

Training rewards
Model marketplace
Data attribution

Rationale

Why Per-User Models?

Every user is unique:

Personalization: Models adapt to individual needs
Privacy: User data stays with user
Ownership: Users own their AI assistant
Portability: Take your model anywhere
Monetization: Sell or license your model

Why Immutable Ledger?

Transparency: Audit trail of all training
Attribution: Credit data contributors
Evolution: Track model improvements
Compliance: Regulatory requirements
Trust: Verifiable training history

Test Cases

Unit Tests

def test_training_record_creation():
    """Test creating an immutable training record"""
    record = TrainingRecord(
        model_id="zen-0.6b",
        dataset_hash="0x1234...abcd",
        training_config={"epochs": 10, "batch_size": 32},
        contributor_proofs=[]
    )
    assert record.timestamp > 0
    assert record.hash is not None

def test_contribution_proof_verification():
    """Test ZK proof verification for data contribution"""
    proof = generate_contribution_proof(
        data_hash="0xdata...",
        contributor_id="0xuser...",
        contribution_type="training_data"
    )
    assert verify_contribution_proof(proof)

def test_immutability_guarantee():
    """Test that records cannot be modified after creation"""
    ledger = TrainingLedger()
    record_id = ledger.add_record(training_record)

    with pytest.raises(ImmutabilityError):
        ledger.modify_record(record_id, new_data)

def test_attribution_chain():
    """Test data contributor attribution tracking"""
    chain = AttributionChain()
    chain.add_contributor("alice", proof_alice)
    chain.add_contributor("bob", proof_bob)

    attributions = chain.get_attributions("model_v1")
    assert len(attributions) == 2
    assert attributions[0].verified

Integration Tests

End-to-End Training Flow: Submit training job → Record creation → Verification
Multi-Contributor Attribution: Multiple data sources → Proper credit distribution
Privacy Verification: ZK proof generation and verification across HIP-4 gateway
Cross-Chain Recording: Training on A-Chain → Record on Hanzo ledger

Security Considerations

Privacy

Zero-knowledge proofs hide data
Homomorphic encryption for aggregation
User controls data access

Security

Post-quantum signatures
TEE for sensitive operations
Secure multi-party computation

Backwards Compatibility

This LP introduces additive interfaces and on-chain records. Existing applications remain compatible; adoption is opt‑in and can be rolled out per contract or service without breaking prior behavior.

Immutable Training Ledger for Privacy-Preserving AI