HealthID Privacy Metrics with Homomorphic Encryption

This repository contains a Python implementation for assessing privacy metrics in a blockchain-based health identity system. The project focuses on quantifying user privacy levels and data leakage using a structured approach. It also explores the application of homomorphic encryption to enhance data privacy while maintaining functionality.

Features

• Privacy Metrics Calculation: Implements metrics to evaluate user privacy, including User Privacy Score (UPS) and Data Leakage Index (DLI).
• Homomorphic Encryption Impact Assessment: Measures the effectiveness of homomorphic encryption in preserving data privacy.
• Data Encryption and Decryption: Utilizes the cryptography library for secure handling of sensitive health information.

Technologies Used

• Python (Flask, Pycryptodome)
• Solidity (v0.8.2)
• Ganache (v2.7.1)
• Web Technologies (HTML, CSS, Javascript - ether.js, Hardhat)
• Chrom Extension (MetaMask)

Installation

Install hardhat with npm

    npx install hardhat
    npm install --save-dev @nomiclabs/hardhat-ethers ethers
    npx hardhat compile
    npx hardhat run scripts/deploy.js --network ganache

Install Python Libraries with pip

    pip install Flask
    pip install pycryptodome

Screenshots