https://github.com/yujidong/Risk-HedgingProtocol

Trustworthy data trading with game-theoretic pricing, blockchain settlement, and TEE validation. Official implementation for IEEE IoT Journal submission.
https://github.com/yujidong/Risk-HedgingProtocol

blockchain data-trading ethereum game-theory hardhat iot machine-learning pytorch sgx smart-contracts trusted-execution-environment

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Trustworthy data trading with game-theoretic pricing, blockchain settlement, and TEE validation. Official implementation for IEEE IoT Journal submission.

• Host: GitHub
• URL: https://github.com/yujidong/Risk-HedgingProtocol
• Owner: yujidong
• License: other
• Created: 2025-12-15T11:52:02.000Z (7 months ago)
• Default Branch: master
• Last Pushed: 2025-12-15T12:44:15.000Z (7 months ago)
• Last Synced: 2025-12-26T23:39:31.253Z (6 months ago)
• Topics: blockchain, data-trading, ethereum, game-theory, hardhat, iot, machine-learning, pytorch, sgx, smart-contracts, trusted-execution-environment
• Language: Python
• Size: 116 KB
• Stars: 0
• Watchers: 0
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE
  • Security: SECURITY.md

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• Awesome-SGX-Open-Source - https://github.com/yujidong/Risk-HedgingProtocol

README

          
# Risk-Hedging Equity Protocol for IoT Data Trading

A complete blockchain-based implementation of the **Trustworthy Data Equity Protocol** combining smart contracts, TEE-secured computation, and AI-powered data analysis for secure IoT data trading.

> **📄 Paper**: This repository contains the official implementation for the paper *"Trustworthy Data Equity: A Retrospective Risk-Hedging Protocol for High-Entropy IoT Data Assets"* submitted to **IEEE Internet of Things Journal**.

## Overview

This project implements an end-to-end decentralized data trading system with three integrated components:

### 🔗 1. Blockchain Smart Contracts

- **Smart Contract Settlement**: Ethereum-compatible contracts handle order creation, escrow, and atomic payment

- **Multi-role Architecture**: Separate accounts for Owner, Buyer, Seller, and TEE Signer

- **Testnet Deployment**: Production-ready deployment on Sepolia, Arbitrum, and Optimism testnets

- **Comprehensive Benchmarks**: Gas cost and performance analysis across L1/L2 networks

### 🔒 2. TEE (Trusted Execution Environment)

- **Intel SGX Integration**: Secure model inference in hardware-protected enclaves via Gramine

- **Performance Benchmarking**: Native vs TEE overhead analysis (~17% inference overhead)

- **Cloud Deployment**: Automated Azure VM setup scripts for SGX-capable infrastructure

- **Cryptographic Attestation**: ECDSA signature generation for on-chain verification

### 🤖 3. AI/ML Data Analysis

- **LSTM Traffic Prediction**: Deep learning models trained on PeMS traffic dataset

- **Noise Robustness Testing**: Validates protocol resilience to sensor noise and data quality issues

- **Game Theory Experiments**: Data scarcity and random drop scenario analysis

- **Automated Visualization**: Comprehensive plotting and results analysis tools

## 🏗️ Project Structure

```

Risk-HedgingProtocol/

├── contracts/                    # 📜 Smart Contracts

│   └── protocol.sol              #    DataEquityProtocol (Solidity)

│

├── test/                         # 🧪 Contract Testing

│   ├── DataEquityProtocol.test.js          # Functional tests

│   ├── DataEquityProtocol.benchmark.js     # L1 performance benchmarks

│   └── DataEquityProtocol.benchmark-simple.js

│

├── scripts/                      # 🛠️ Utility Scripts

│   ├── check-all-balances.js    # Multi-account balance checker

│   ├── compare_benchmarks.py    # Benchmark results comparison

│   └── run_all_benchmarks.ps1   # Automated test runner

│

├── tee/                          # 🔒 TEE Component (NEW!)

│   ├── tee_benchmark.py         # Main TEE benchmark script

│   ├── Dockerfile.gramine       # SGX container configuration

│   ├── tee_benchmark.manifest.template # Gramine SGX manifest

│   ├── create_azure_vm.ps1      # Azure SGX VM provisioning

│   ├── run_cloud_test.sh        # Automated TEE testing

│   ├── data/                    # PEMS08 dataset (shared with ML)

│   ├── models/                  # Trained LSTM models

│   ├── results/                 # TEE benchmark results

│   └── docs/                    # TEE architecture documentation

│

├── input/                        # 📊 ML Datasets

│   └── pems-dataset/            # PeMS traffic dataset (PEMS03/04/07/08)

│

├── output/                       # 📈 Experiment Results

│   ├── data/                    # ML experiment results (JSON)

│   ├── figures/                 # Visualization plots (PNG)

│   └── benchmark/               # Blockchain benchmark results

│

├── noise_robustness_experiment.py # 🤖 Main ML experiments

├── game_theory_data_scarcity.py   # Game theory: data scarcity

├── game_theory_random_drop.py     # Game theory: random drops

├── visualize_results.py           # Results visualization

│

├── hardhat.config.ts             # Hardhat configuration (3 networks)

├── ACCOUNTS.md                   # Multi-account setup guide

├── DEPLOYMENT.md                 # Deployment instructions

└── README.md                     # This file

```

## Smart Contract Features

### DataEquityProtocol Contract

**Core Functions:**

- `createOrder()`: Buyer locks funds in escrow with pricing parameters

- `settleTransaction()`: TEE-signed utility score triggers atomic payment

- `refund()`: Timeout protection for buyers

- `setTEESigner()`: Admin function to update TEE public key

**Pricing Model:**

```

Final Payment = p_base + α * k * u

Where:

- p_base: Base fee (fixed)

- α: Equity share coefficient (0 ≤ α ≤ 1)

- k: Utility-to-money conversion factor

- u: Data utility score (0 ≤ u ≤ 1, TEE-verified)

```

**Security:**

- ECDSA signature verification for TEE attestation

- ReentrancyGuard protection

- Ownable access control

- Nonce-based replay attack prevention

## 🚀 Quick Start Guide

### Prerequisites

- **Node.js** 22+ and npm 11+ (blockchain development)

- **Python** 3.9+ with conda (ML experiments)

- **Docker** (optional, for TEE testing)

- **Azure Account** (optional, for SGX cloud deployment)

### 1️⃣ Blockchain Setup

```bash

# Install dependencies

npm install

# Compile contracts

npx hardhat compile

# Run functional tests

npx hardhat test

# Run performance benchmarks (requires testnet ETH)

npx hardhat test test/DataEquityProtocol.benchmark.js --network sepolia

```

### 2️⃣ ML/AI Setup

```bash

# Create conda environment

conda env create -f environment.yml

conda activate risk-hedging

# Run noise robustness experiments

python noise_robustness_experiment.py

# Run game theory analysis

python game_theory_data_scarcity.py

python game_theory_random_drop.py

# Generate visualizations

python visualize_results.py

```

### 3️⃣ TEE Setup (Advanced)

```bash

cd tee/

# Option A: Local testing with dummy data

python tee_benchmark.py --mode native --iterations 10

# Option B: Cloud SGX deployment

# 1. Create Azure VM with SGX support

.\create_azure_vm.ps1

# 2. Deploy and run tests

.\redeploy_all.ps1

# 3. SSH to VM and execute

ssh azureuser@

chmod +x run_cloud_test.sh

./run_cloud_test.sh

# Results will be in tee/results/*.json

```

## 📊 Complete Workflow Example

### End-to-End Data Trading Simulation

**Step 1: Generate Training Data**

```bash

# ML experiments produce utility scores and model performance

python noise_robustness_experiment.py

# Output: output/data/*.json

```

**Step 2: Deploy Smart Contract**

```bash

# Set up accounts and deploy to testnet

npx hardhat keystore set SEPOLIA_PRIVATE_KEY --dev

npx hardhat ignition deploy ignition/modules/DataEquityProtocol.ts --network sepolia

# Contract address: 0xE0aa880da6822A26C946f9417F7F6380FDf9799F (example)

```

**Step 3: TEE Utility Evaluation**

```bash

cd tee/

# TEE validates data and signs utility score

python tee_benchmark.py --mode native

# Output: Utility score (0.80) + ECDSA signature

```

**Step 4: On-Chain Settlement**

```bash

# Buyer creates order with locked funds

# TEE submits signed utility score

# Smart contract verifies signature and executes payment

npx hardhat test test/DataEquityProtocol.test.js --network sepolia

# ✅ Order settled: Seller receives payment based on utility

```

**Step 5: Analyze Results**

```bash

# Compare blockchain benchmarks

python scripts/compare_benchmarks.py

# Visualize all experimental data

python visualize_results.py

```

## 📖 Detailed Usage

## 📖 Detailed Usage

### Blockchain Component

#### Deploy to Public Testnet

```bash

# Configure accounts (Owner, Buyer, Seller, TEE Signer)

# See ACCOUNTS.md for multi-account setup guide

npx hardhat keystore set SEPOLIA_PRIVATE_KEY

npx hardhat keystore set SEPOLIA_PRIVATE_KEY_2

npx hardhat keystore set SEPOLIA_PRIVATE_KEY_3

npx hardhat keystore set SEPOLIA_PRIVATE_KEY_4

# Check balances

npx hardhat run scripts/check-all-balances.js --network sepolia

# Deploy contract

npx hardhat ignition deploy ignition/modules/DataEquityProtocol.ts --network sepolia

# Run comprehensive benchmarks

npx hardhat test test/DataEquityProtocol.benchmark.js --network sepolia

```

**Benchmark Results** (Sepolia L1 @ 3 gwei):

- Deployment: 21,000 gas (~$0.06)

- Order Creation: 273,077 gas (~$2.46)

- Settlement: 73,708 gas (~$0.63)

- Refund: 44,745 gas (~$0.41)

See [DEPLOYMENT.md](DEPLOYMENT.md) for complete deployment guide.

### AI/ML Component

#### LSTM Noise Robustness Experiments

```bash

# Train and evaluate LSTM models with noise injection

python noise_robustness_experiment.py

# Key parameters:

# - Noise levels: 0%, 10%, 20%, 30%, 40%, 50%

# - Datasets: PEMS03, PEMS04, PEMS07, PEMS08

# - Model: 2-layer LSTM (hidden_size=256)

# Output: output/data/*_results.json

# Metrics: MAE, RMSE, R2 score per noise level

```

#### Game Theory Experiments

```bash

# Data scarcity scenarios (50-100% data availability)

python game_theory_data_scarcity.py

# Random drop scenarios (probability-based data loss)

python game_theory_random_drop.py

# Generate comparison plots

python visualize_results.py

```

### TEE Component

#### Local Testing (Without SGX Hardware)

```bash

cd tee/

# Create dummy data for testing

python tee_benchmark.py --mode native --iterations 10 --use-dummy-data

# Output: results/native_*.json

```

#### Cloud SGX Deployment (Production)

**Prerequisites**:

- Azure account with quota for DC-series VMs

- Azure CLI installed and authenticated

- SSH key pair configured

**Deployment Steps**:

```bash

cd tee/

# 1. Provision Azure SGX VM

.\create_azure_vm.ps1

# Creates: Standard_DC2s_v3 VM with 8GB SGX EPC

# 2. Deploy code and dependencies

.\redeploy_all.ps1

# Uploads: tee_benchmark.py, data/, models/, configs

# 3. SSH to VM and run tests

ssh azureuser@

# 4. Run native baseline

docker run --rm -v $(pwd):/app tee-benchmark:native python tee_benchmark.py

# 5. Run SGX enclave test

docker run --device /dev/sgx_enclave --device /dev/sgx_provision \

  -v $(pwd):/app tee-benchmark:sgx gramine-sgx python

# 6. Retrieve results

exit

scp azureuser@:~/results/*.json ./results/

# 7. Stop VM to avoid charges

az vm deallocate --resource-group web3 --name tee-benchmark-vm

```

**TEE Performance Results**:

- Native Inference: 547ms ± 18ms

- SGX Inference: 642ms ± 5ms (+17.3% overhead)

- Model Accuracy: Identical (R² = 0.8063)

- Memory Overhead: +18.2%

See [tee/docs/ARCHITECTURE.md](tee/docs/ARCHITECTURE.md) for technical details.

## 🧪 Experimental Results

## 🧪 Experimental Results Summary

### 1. Blockchain Performance (Sepolia L1 Testnet)

| Operation | Gas Used | Cost (@3000 gwei) | Time |

|-----------|----------|-------------------|------|

| Contract Deployment | 21,000 | $0.06 | ~11s |

| Order Creation | 273,077 | $2.46 | ~2s |

| Order Settlement | 73,708 | $0.63 | ~2s |

| Refund | 44,745 | $0.41 | ~2s |

| **Complete Trade** | **346,785** | **$3.09** | **~4s** |

💡 **L2 Recommendation**: Deploying to Arbitrum/Optimism can reduce costs by 95% (~$0.15/trade)

### 2. TEE Performance (Azure DC2s_v3 + Intel SGX)

| Metric | Native Docker | Gramine SGX | Overhead |

|--------|---------------|-------------|----------|

| Inference Time | 547ms ± 18ms | 642ms ± 5ms | **+17.3%** |

| Model Accuracy (R²) | 0.8063 | 0.8063 | **Identical** |

| Memory Usage | 2047 MB | 2420 MB | +18.2% |

| Signature Generation | 13ms ± 3ms | 8ms ± 2ms | -38% |

🔒 **Key Finding**: Only **17% inference overhead** in real SGX hardware with **identical accuracy**

### 3. AI/ML Noise Robustness

**LSTM Model Performance under Noise:**

| Noise Level | MAE | RMSE | R² Score | Utility Score |

|-------------|-----|------|----------|---------------|

| 0% (Clean) | 2.87 | 4.12 | 0.953 | 1.00 |

| 10% | 3.02 | 4.31 | 0.945 | 0.95 |

| 20% | 3.24 | 4.58 | 0.932 | 0.89 |

| 30% | 3.51 | 4.91 | 0.915 | 0.81 |

| 40% | 3.89 | 5.34 | 0.891 | 0.72 |

| 50% | 4.42 | 5.98 | 0.852 | 0.61 |

✅ **Protocol Stability**: Maintains reliable utility scores up to 40% noise level

**Game Theory Results**:

- Data scarcity: Linear degradation from 100% → 50% data availability

- Random drops: Exponential impact on utility with drop probability > 0.3

All results available in `output/data/*.json` and `tee/results/*.json`

## 🏛️ System Architecture



  



## 🧩 Key Components Integration

### Data Flow

1. **Data Generation** (ML Component)

   - IoT sensors → PeMS traffic dataset

   - LSTM training & validation

   - Noise injection experiments

   - Output: Utility metrics (R², MAE, RMSE)

2. **TEE Validation** (Security Component)

   - Load trained LSTM model into SGX enclave

   - Secure inference on encrypted data

   - Generate utility score (u = 0.0 to 1.0)

   - Sign with TEE private key → ECDSA signature

3. **Blockchain Settlement** (Smart Contract)

   - Buyer creates order: locks `maxDeposit` ETH

   - Sets pricing: `p_base`, `α`, `k`

   - TEE submits: `(orderId, utility, signature)`

   - Contract verifies signature

   - Calculates: `payment = p_base + α * k * u`

   - Transfers to Seller, refunds excess to Buyer

### Pricing Formula

```

P = p_base + α × k × u

Where:

- p_base: Base fee (guaranteed minimum payment)

- α: Equity share [0, 1] (risk-sharing coefficient)

- k: Utility-to-money conversion factor (scaling parameter)

- u: Data utility score [0, 1] (TEE-verified quality metric)

```

**Example**:

- `p_base = 0.01 ETH`, `α = 0.5`, `k = 0.005 ETH`, `u = 0.95`

- **Payment** = 0.01 + 0.5 × 0.005 × 0.95 = **0.012375 ETH**

## 🛠️ Technology Stack

**Blockchain:**

- Solidity 0.8.28 - Smart contract language

- Hardhat 3.1.0 - Development framework

- Viem 2.41.2 - Type-safe Ethereum library

- OpenZeppelin - Security-audited contracts

- Sepolia/Arbitrum/Optimism - Multi-testnet support

**TEE:**

- Intel SGX - Hardware-based trusted execution

- Gramine 1.7+ - SGX library OS

- Docker - Containerization

- Azure DC-series - SGX-capable VMs

**AI/ML:**

- PyTorch 2.5.1 - Deep learning framework

- CUDA 12.4 - GPU acceleration

- scikit-learn - ML utilities

- NumPy/Pandas - Data processing

- Matplotlib - Visualization

## 🔐 Security Features

1. **TEE Signature Verification**: ECDSA signature validation for utility scores

2. **Escrow Protection**: Funds locked until settlement or timeout refund

3. **Replay Attack Prevention**: Unique nonce per transaction

4. **Reentrancy Guard**: Protection against recursive call attacks

5. **Access Control**: Owner-only admin functions (Ownable pattern)

6. **Atomic Settlement**: All-or-nothing payment execution

## 🚧 Future Enhancements

- [ ] Multi-TEE consensus mechanism

- [ ] Layer 2 deployment (Arbitrum/Optimism mainnet)

- [ ] Real-time IoT device integration

- [ ] Dynamic pricing with market-based α

- [ ] Cross-chain settlement support

- [ ] Advanced data quality metrics

## 📄 License

MIT License - See [LICENSE](LICENSE)

## 🔗 Useful Links

**Testnet Faucets:**

- Sepolia: https://sepoliafaucet.com/

- Arbitrum Sepolia: https://bridge.arbitrum.io/

- Optimism Sepolia: https://app.optimism.io/bridge

**Block Explorers:**

- Sepolia: https://sepolia.etherscan.io/

- Arbitrum: https://sepolia.arbiscan.io/

- Optimism: https://sepolia-optimism.etherscan.io/

**Datasets:**

- PeMS Traffic: http://pems.dot.ca.gov/

- Azure SGX Docs: https://learn.microsoft.com/en-us/azure/virtual-machines/dcv3-series

---