feat: add marketplace metrics, privacy features, and service registry endpoints
- Add Prometheus metrics for marketplace API throughput and error rates with new dashboard panels - Implement confidential transaction models with encryption support and access control - Add key management system with registration, rotation, and audit logging - Create services and registry routers for service discovery and management - Integrate ZK proof generation for privacy-preserving receipts - Add metrics instru
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# Zero-Knowledge Applications Research Plan
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## Executive Summary
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This research plan explores advanced zero-knowledge (ZK) applications for the AITBC platform, focusing on privacy-preserving AI computations, verifiable machine learning, and scalable ZK proof systems. The research aims to make AITBC the leading platform for privacy-preserving AI/ML workloads while advancing the state of ZK technology through novel circuit designs and optimization techniques.
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## Research Objectives
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### Primary Objectives
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1. **Enable Private AI Inference** without revealing models or data
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2. **Implement Verifiable ML** with proof of correct computation
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3. **Scale ZK Proofs** to handle large AI models efficiently
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4. **Create ZK Dev Tools** for easy application development
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5. **Standardize ZK Protocols** for interoperability
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### Secondary Objectives
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1. **Reduce Proof Generation Time** by 90% through optimization
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2. **Support Recursive Proofs** for complex workflows
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3. **Enable ZK Rollups** with AI-specific optimizations
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4. **Create ZK Marketplace** for privacy-preserving services
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5. **Develop ZK Identity** for anonymous AI agents
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## Technical Architecture
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### ZK Stack Architecture
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```
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┌─────────────────────────────────────────────────────────────┐
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│ Application Layer │
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│ ┌─────────────┐ ┌──────────────┐ ┌─────────────────────┐ │
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│ │ AI/ML │ │ DeFi │ │ Identity │ │
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│ │ Services │ │ Applications │ │ Systems │ │
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│ └─────────────┘ └──────────────┘ └─────────────────────┘ │
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├─────────────────────────────────────────────────────────────┤
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│ ZK Abstraction Layer │
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│ ┌─────────────┐ ┌──────────────┐ ┌─────────────────────┐ │
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│ │ Circuit │ │ Proof │ │ Verification │ │
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│ │ Builder │ │ Generator │ │ Engine │ │
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│ └─────────────┘ └──────────────┘ └─────────────────────┘ │
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├─────────────────────────────────────────────────────────────┤
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│ Core ZK Infrastructure │
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│ ┌─────────────┐ ┌──────────────┐ ┌─────────────────────┐ │
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│ │ Groth16 │ │ PLONK │ │ Halo2 │ │
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│ │ Prover │ │ Prover │ │ Prover │ │
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│ └─────────────┘ └──────────────┘ └─────────────────────┘ │
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└─────────────────────────────────────────────────────────────┘
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```
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### AI-Specific ZK Applications
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```
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┌─────────────────────────────────────────────────────────────┐
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│ Privacy-Preserving AI │
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│ │
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│ Input Data ──┐ │
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│ ├───► ZK Circuit ──┐ │
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│ Model Weights─┘ │ │
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│ ├───► ZK Proof ──► Result │
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│ Computation ──────────────────┘ │
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│ │
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│ ✓ Private inference without revealing model │
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│ ✓ Verifiable computation with proof │
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│ ✓ Composable proofs for complex workflows │
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└─────────────────────────────────────────────────────────────┘
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```
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## Research Methodology
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### Phase 1: Foundation (Months 1-2)
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#### 1.1 ZK Circuit Design for AI
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- **Neural Network Circuits**: Efficient ZK circuits for common layers
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- **Optimization Techniques**: Reducing constraint count
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- **Lookup Tables**: Optimizing non-linear operations
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- **Recursive Composition**: Building complex proofs from simple ones
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#### 1.2 Proof System Optimization
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- **Prover Performance**: GPU/ASIC acceleration
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- **Verifier Efficiency**: Constant-time verification
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- **Proof Size**: Minimizing proof bandwidth
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- **Parallelization**: Multi-core proving strategies
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#### 1.3 Privacy Model Design
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- **Data Privacy**: Protecting input/output data
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- **Model Privacy**: Protecting model parameters
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- **Computation Privacy**: Hiding computation patterns
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- **Composition Privacy**: Composable privacy guarantees
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### Phase 2: Implementation (Months 3-4)
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#### 2.1 Core ZK Library
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```python
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class ZKProver:
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def __init__(self, proving_system: ProvingSystem):
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self.proving_system = proving_system
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self.circuit_cache = CircuitCache()
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self.proving_key_cache = ProvingKeyCache()
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async def prove_inference(
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self,
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model: NeuralNetwork,
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input_data: Tensor,
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witness: Optional[Tensor] = None
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) -> ZKProof:
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"""Generate ZK proof for model inference"""
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# Build or retrieve circuit
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circuit = await self.circuit_cache.get_or_build(model)
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# Generate witness
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if witness is None:
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witness = await self.generate_witness(model, input_data)
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# Load proving key
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proving_key = await self.proving_key_cache.get(circuit.id)
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# Generate proof
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proof = await self.proving_system.prove(
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circuit, witness, proving_key
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)
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return proof
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async def verify_inference(
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self,
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proof: ZKProof,
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public_inputs: PublicInputs,
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circuit_id: str
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) -> bool:
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"""Verify ZK proof of inference"""
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# Load verification key
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verification_key = await self.load_verification_key(circuit_id)
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# Verify proof
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return await self.proving_system.verify(
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proof, public_inputs, verification_key
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)
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class AICircuitBuilder:
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def __init__(self):
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self.layer_builders = {
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'dense': self.build_dense_layer,
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'conv2d': self.build_conv2d_layer,
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'relu': self.build_relu_layer,
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'batch_norm': self.build_batch_norm_layer,
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}
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async def build_circuit(self, model: NeuralNetwork) -> Circuit:
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"""Build ZK circuit for neural network"""
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circuit = Circuit()
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# Build layers sequentially
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for layer in model.layers:
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layer_type = layer.type
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builder = self.layer_builders[layer_type]
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circuit = await builder(circuit, layer)
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# Add constraints for input/output privacy
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circuit = await self.add_privacy_constraints(circuit)
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return circuit
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async def build_dense_layer(
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self,
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circuit: Circuit,
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layer: DenseLayer
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) -> Circuit:
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"""Build ZK circuit for dense layer"""
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# Create variables for weights and inputs
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weights = circuit.create_private_variables(layer.weight_shape)
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inputs = circuit.create_private_variables(layer.input_shape)
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# Matrix multiplication constraints
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outputs = []
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for i in range(layer.output_size):
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weighted_sum = circuit.create_linear_combination(
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weights[i], inputs
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)
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output = circuit.add_constraint(
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weighted_sum + layer.bias[i],
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"dense_output"
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)
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outputs.append(output)
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return circuit
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```
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#### 2.2 Privacy-Preserving Inference
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```python
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class PrivateInferenceService:
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def __init__(self, zk_prover: ZKProver, model_store: ModelStore):
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self.zk_prover = zk_prover
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self.model_store = model_store
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async def private_inference(
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self,
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model_id: str,
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encrypted_input: EncryptedData,
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privacy_requirements: PrivacyRequirements
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) -> InferenceResult:
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"""Perform private inference with ZK proof"""
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# Decrypt input (only for computation)
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input_data = await self.decrypt_input(encrypted_input)
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# Load model (encrypted at rest)
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model = await self.model_store.load_encrypted(model_id)
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# Perform inference
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raw_output = await model.forward(input_data)
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# Generate ZK proof
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proof = await self.zk_prover.prove_inference(
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model, input_data
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)
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# Create result with proof
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result = InferenceResult(
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output=raw_output,
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proof=proof,
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model_id=model_id,
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timestamp=datetime.utcnow()
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)
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return result
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async def verify_inference(
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self,
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result: InferenceResult,
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public_commitments: PublicCommitments
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) -> bool:
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"""Verify inference result without learning output"""
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# Verify ZK proof
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proof_valid = await self.zk_prover.verify_inference(
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result.proof,
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public_commitments,
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result.model_id
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)
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return proof_valid
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```
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#### 2.3 Verifiable Machine Learning
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```python
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class VerifiableML:
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def __init__(self, zk_prover: ZKProver):
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self.zk_prover = zk_prover
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async def prove_training(
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self,
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dataset: Dataset,
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model: NeuralNetwork,
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training_params: TrainingParams
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) -> TrainingProof:
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"""Generate proof of correct training"""
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# Create training circuit
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circuit = await self.create_training_circuit(
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dataset, model, training_params
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)
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# Generate witness from training process
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witness = await self.generate_training_witness(
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dataset, model, training_params
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)
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# Generate proof
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proof = await self.zk_prover.prove_training(circuit, witness)
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return TrainingProof(
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proof=proof,
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model_hash=model.hash(),
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dataset_hash=dataset.hash(),
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metrics=training_params.metrics
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)
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async def prove_model_integrity(
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self,
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model: NeuralNetwork,
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expected_architecture: ModelArchitecture
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) -> IntegrityProof:
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"""Proof that model matches expected architecture"""
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# Create architecture verification circuit
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circuit = await self.create_architecture_circuit(
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expected_architecture
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)
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# Generate witness from model
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witness = await self.extract_model_witness(model)
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# Generate proof
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proof = await self.zk_prover.prove(circuit, witness)
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return IntegrityProof(
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proof=proof,
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architecture_hash=expected_architecture.hash()
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)
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```
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### Phase 3: Advanced Applications (Months 5-6)
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#### 3.1 ZK Rollups for AI
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```python
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class ZKAIRollup:
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def __init__(self, layer1: Layer1, zk_prover: ZKProver):
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self.layer1 = layer1
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self.zk_prover = zk_prover
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self.state = RollupState()
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async def submit_batch(
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self,
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operations: List[AIOperation]
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) -> BatchProof:
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"""Submit batch of AI operations to rollup"""
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# Create batch circuit
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circuit = await self.create_batch_circuit(operations)
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# Generate witness
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witness = await self.generate_batch_witness(
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operations, self.state
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)
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# Generate proof
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proof = await self.zk_prover.prove_batch(circuit, witness)
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# Submit to Layer 1
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await self.layer1.submit_ai_batch(proof, operations)
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return BatchProof(proof=proof, operations=operations)
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async def create_batch_circuit(
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self,
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operations: List[AIOperation]
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) -> Circuit:
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"""Create circuit for batch of operations"""
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circuit = Circuit()
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# Add constraints for each operation
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for op in operations:
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if op.type == "inference":
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circuit = await self.add_inference_constraints(
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circuit, op
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)
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elif op.type == "training":
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circuit = await self.add_training_constraints(
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circuit, op
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)
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elif op.type == "model_update":
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circuit = await self.add_update_constraints(
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circuit, op
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)
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# Add batch-level constraints
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circuit = await self.add_batch_constraints(circuit, operations)
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return circuit
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```
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#### 3.2 ZK Identity for AI Agents
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```python
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class ZKAgentIdentity:
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def __init__(self, zk_prover: ZKProver):
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self.zk_prover = zk_prover
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self.identity_registry = IdentityRegistry()
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|
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async def create_agent_identity(
|
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self,
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agent_capabilities: AgentCapabilities,
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reputation_data: ReputationData
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) -> AgentIdentity:
|
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"""Create ZK identity for AI agent"""
|
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|
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# Create identity circuit
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circuit = await self.create_identity_circuit()
|
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|
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# Generate commitment to capabilities
|
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capability_commitment = await self.commit_to_capabilities(
|
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agent_capabilities
|
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)
|
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|
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# Generate ZK proof of capabilities
|
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proof = await self.zk_prover.prove_capabilities(
|
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circuit, agent_capabilities, capability_commitment
|
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)
|
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|
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# Create identity
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identity = AgentIdentity(
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commitment=capability_commitment,
|
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proof=proof,
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nullifier=self.generate_nullifier(),
|
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created_at=datetime.utcnow()
|
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)
|
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|
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# Register identity
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await self.identity_registry.register(identity)
|
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|
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return identity
|
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|
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async def prove_capability(
|
||||
self,
|
||||
identity: AgentIdentity,
|
||||
required_capability: str,
|
||||
proof_data: Any
|
||||
) -> CapabilityProof:
|
||||
"""Proof that agent has required capability"""
|
||||
|
||||
# Create capability proof circuit
|
||||
circuit = await self.create_capability_circuit(required_capability)
|
||||
|
||||
# Generate witness
|
||||
witness = await self.generate_capability_witness(
|
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identity, proof_data
|
||||
)
|
||||
|
||||
# Generate proof
|
||||
proof = await self.zk_prover.prove_capability(circuit, witness)
|
||||
|
||||
return CapabilityProof(
|
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identity_commitment=identity.commitment,
|
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capability=required_capability,
|
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proof=proof
|
||||
)
|
||||
```
|
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|
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### Phase 4: Optimization & Scaling (Months 7-8)
|
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|
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#### 4.1 Proof Generation Optimization
|
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- **GPU Acceleration**: CUDA kernels for constraint solving
|
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- **Distributed Proving**: Multi-machine proof generation
|
||||
- **Circuit Specialization**: Hardware-specific optimizations
|
||||
- **Memory Optimization**: Efficient memory usage patterns
|
||||
|
||||
#### 4.2 Verification Optimization
|
||||
- **Recursive Verification**: Batch verification of proofs
|
||||
- **SNARK-friendly Hashes**: Efficient hash functions
|
||||
- **Aggregated Signatures**: Reduce verification overhead
|
||||
- **Lightweight Clients**: Mobile-friendly verification
|
||||
|
||||
#### 4.3 Storage Optimization
|
||||
- **Proof Compression**: Efficient proof encoding
|
||||
- **Circuit Caching**: Reuse of common circuits
|
||||
- **State Commitments**: Efficient state proofs
|
||||
- **Archival Strategies**: Long-term proof storage
|
||||
|
||||
## Technical Specifications
|
||||
|
||||
### Performance Targets
|
||||
|
||||
| Metric | Current | Target | Improvement |
|
||||
|--------|---------|--------|-------------|
|
||||
| Proof Generation | 10 minutes | 1 minute | 10x |
|
||||
| Proof Size | 1MB | 100KB | 10x |
|
||||
| Verification Time | 100ms | 10ms | 10x |
|
||||
| Supported Model Size | 10MB | 1GB | 100x |
|
||||
| Concurrent Proofs | 10 | 1000 | 100x |
|
||||
|
||||
### Supported Operations
|
||||
|
||||
| Operation | ZK Support | Privacy Level | Performance |
|
||||
|-----------|------------|---------------|-------------|
|
||||
| Inference | ✓ | Full | High |
|
||||
| Training | ✓ | Partial | Medium |
|
||||
| Model Update | ✓ | Full | High |
|
||||
| Data Sharing | ✓ | Full | High |
|
||||
| Reputation | ✓ | Partial | High |
|
||||
|
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### Circuit Library
|
||||
|
||||
| Circuit Type | Constraints | Use Case | Optimization |
|
||||
|--------------|-------------|----------|-------------|
|
||||
| Dense Layer | 10K-100K | Standard NN | Lookup Tables |
|
||||
| Convolution | 100K-1M | CNN | Winograd |
|
||||
| Attention | 1M-10M | Transformers | Sparse |
|
||||
| Pooling | 1K-10K | CNN | Custom |
|
||||
| Activation | 1K-10K | All | Lookup |
|
||||
|
||||
## Security Analysis
|
||||
|
||||
### Privacy Guarantees
|
||||
|
||||
#### 1. Input Privacy
|
||||
- **Zero-Knowledge**: Proofs reveal nothing about inputs
|
||||
- **Perfect Secrecy**: Information-theoretic privacy
|
||||
- **Composition**: Privacy preserved under composition
|
||||
|
||||
#### 2. Model Privacy
|
||||
- **Weight Encryption**: Model parameters encrypted
|
||||
- **Circuit Obfuscation**: Circuit structure hidden
|
||||
- **Access Control**: Fine-grained permissions
|
||||
|
||||
#### 3. Computation Privacy
|
||||
- **Timing Protection**: Constant-time operations
|
||||
- **Access Pattern**: ORAM for memory access
|
||||
- **Side-Channel**: Resistant to side-channel attacks
|
||||
|
||||
### Security Properties
|
||||
|
||||
#### 1. Soundness
|
||||
- **Computational**: Infeasible to forge invalid proofs
|
||||
- **Statistical**: Negligible soundness error
|
||||
- **Universal**: Works for all valid inputs
|
||||
|
||||
#### 2. Completeness
|
||||
- **Perfect**: All valid proofs verify
|
||||
- **Efficient**: Fast verification
|
||||
- **Robust**: Tolerates noise
|
||||
|
||||
#### 3. Zero-Knowledge
|
||||
- **Perfect**: Zero information leakage
|
||||
- **Simulation**: Simulator exists
|
||||
- **Composition**: Composable ZK
|
||||
|
||||
## Implementation Plan
|
||||
|
||||
### Phase 1: Foundation (Months 1-2)
|
||||
- [ ] Complete ZK circuit library design
|
||||
- [ ] Implement core prover/verifier
|
||||
- [ ] Create privacy model framework
|
||||
- [ ] Set up development environment
|
||||
|
||||
### Phase 2: Core Features (Months 3-4)
|
||||
- [ ] Implement private inference
|
||||
- [ ] Build verifiable ML system
|
||||
- [ ] Create ZK rollup for AI
|
||||
- [ ] Develop ZK identity system
|
||||
|
||||
### Phase 3: Advanced Features (Months 5-6)
|
||||
- [ ] Add recursive proofs
|
||||
- [ ] Implement distributed proving
|
||||
- [ ] Create ZK marketplace
|
||||
- [ ] Build developer SDK
|
||||
|
||||
### Phase 4: Optimization (Months 7-8)
|
||||
- [ ] GPU acceleration
|
||||
- [ ] Proof compression
|
||||
- [ ] Verification optimization
|
||||
- [ ] Storage optimization
|
||||
|
||||
### Phase 5: Integration (Months 9-12)
|
||||
- [ ] Integrate with AITBC
|
||||
- [ ] Deploy testnet
|
||||
- [ ] Developer onboarding
|
||||
- [ ] Mainnet launch
|
||||
|
||||
## Deliverables
|
||||
|
||||
### Technical Deliverables
|
||||
1. **ZK Circuit Library** (Month 2)
|
||||
2. **Private Inference System** (Month 4)
|
||||
3. **ZK Rollup Implementation** (Month 6)
|
||||
4. **Optimized Prover** (Month 8)
|
||||
5. **Mainnet Integration** (Month 12)
|
||||
|
||||
### Research Deliverables
|
||||
1. **Conference Papers**: 3 papers on ZK for AI
|
||||
2. **Technical Reports**: Quarterly progress
|
||||
3. **Open Source**: All code under MIT license
|
||||
4. **Standards**: ZK protocol specifications
|
||||
|
||||
### Developer Deliverables
|
||||
1. **SDK**: Multi-language development kit
|
||||
2. **Documentation**: Comprehensive guides
|
||||
3. **Examples**: AI/ML use cases
|
||||
4. **Tools**: Circuit compiler, debugger
|
||||
|
||||
## Resource Requirements
|
||||
|
||||
### Team
|
||||
- **Principal Investigator** (1): ZK cryptography expert
|
||||
- **Cryptography Engineers** (3): ZK system implementation
|
||||
- **AI/ML Engineers** (2): AI circuit design
|
||||
- **Systems Engineers** (2): Performance optimization
|
||||
- **Security Researchers** (2): Security analysis
|
||||
- **Developer Advocate** (1): Developer tools
|
||||
|
||||
### Infrastructure
|
||||
- **GPU Cluster**: 100 GPUs for proving
|
||||
- **Compute Nodes**: 50 CPU nodes for verification
|
||||
- **Storage**: 100TB for model storage
|
||||
- **Network**: High-bandwidth for data transfer
|
||||
|
||||
### Budget
|
||||
- **Personnel**: $7M
|
||||
- **Infrastructure**: $2M
|
||||
- **Research**: $1M
|
||||
- **Community**: $1M
|
||||
|
||||
## Success Metrics
|
||||
|
||||
### Technical Metrics
|
||||
- [ ] Achieve 1-minute proof generation
|
||||
- [ ] Support 1GB+ models
|
||||
- [ ] Handle 1000+ concurrent proofs
|
||||
- [ ] Pass 3 security audits
|
||||
- [ ] 10x improvement over baseline
|
||||
|
||||
### Adoption Metrics
|
||||
- [ ] 100+ AI models using ZK
|
||||
- [ ] 10+ enterprise applications
|
||||
- [ ] 1000+ active developers
|
||||
- [ ] 1M+ ZK proofs generated
|
||||
- [ ] 5+ partnerships
|
||||
|
||||
### Research Metrics
|
||||
- [ ] 3+ papers at top conferences
|
||||
- [ ] 5+ patents filed
|
||||
- [ ] 10+ academic collaborations
|
||||
- [ ] Open source with 10,000+ stars
|
||||
- [ ] Industry recognition
|
||||
|
||||
## Risk Mitigation
|
||||
|
||||
### Technical Risks
|
||||
1. **Proof Complexity**: AI circuits may be too complex
|
||||
- Mitigation: Incremental complexity, optimization
|
||||
2. **Performance**: May not meet performance targets
|
||||
- Mitigation: Hardware acceleration, parallelization
|
||||
3. **Security**: New attack vectors possible
|
||||
- Mitigation: Formal verification, audits
|
||||
|
||||
### Adoption Risks
|
||||
1. **Complexity**: Hard to use for developers
|
||||
- Mitigation: Abstractions, SDK, documentation
|
||||
2. **Cost**: Proving may be expensive
|
||||
- Mitigation: Optimization, subsidies
|
||||
3. **Interoperability**: May not work with other systems
|
||||
- Mitigation: Standards, bridges
|
||||
|
||||
### Research Risks
|
||||
1. **Dead Ends**: Some approaches may not work
|
||||
- Mitigation: Parallel research tracks
|
||||
2. **Obsolescence**: Technology may change
|
||||
- Mitigation: Flexible architecture
|
||||
3. **Competition**: Others may advance faster
|
||||
- Mitigation: Focus on AI specialization
|
||||
|
||||
## Conclusion
|
||||
|
||||
This research plan establishes AITBC as the leader in zero-knowledge applications for AI/ML workloads. The combination of privacy-preserving inference, verifiable machine learning, and scalable ZK infrastructure creates a unique value proposition for the AI community.
|
||||
|
||||
The 12-month timeline with clear deliverables ensures steady progress toward production-ready implementation. The research outcomes will not only benefit AITBC but advance the entire field of privacy-preserving AI.
|
||||
|
||||
By focusing on practical applications and developer experience, we ensure that the research translates into real-world impact, enabling the next generation of privacy-preserving AI applications on blockchain.
|
||||
|
||||
---
|
||||
|
||||
*This research plan will evolve based on technological advances and community feedback. Regular reviews ensure alignment with ecosystem needs.*
|
||||
Reference in New Issue
Block a user