Blockchain Reputation System using On-Chain Data

Español: README.ES.md

This repository contains the source code for the Diploma Thesis: "Blockchain Reputation System using On-Chain Data," presented at the Faculty of Mathematics and Computation, University of Havana.

• Author: José Miguel Zayas Pérez
• Advisors: Lic. Ariel Díaz Pérez, Dr. Miguel Katrib Mora

🔗 Project Link: https://github.com/josem-nex/blockchain-reputation

🔗 Thesis PDF (spanish): PDF

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Project Summary

This project tackles the challenge of quantifying trust on the Blockchain. A data provider has been developed that processes the transactional history of a wallet on EVM networks to extract objective behavioral metrics. The solution uses a smart contract as an on-chain cache layer to drastically optimize performance. Its utility is demonstrated with a prototype system for P2P markets that translates these metrics into an intuitive trust score.

Keywords: Reputation System, Blockchain, On-chain, Smart Contract, Decentralized Trust.

Key Features

• 🔍 On-Chain Data Provider: Analyzes a wallet's history to generate quantitative metrics about its activity.
• ⚡ Smart Contract Cache: Optimizes subsequent analyses using a smart contract and ensures data persistence with public access.
• 📊 Reputation Model (P2P): Includes a use case that assigns a trust score in a P2P market.
• ⚙️ RESTful API and Web UI: Exposes functionality through an API (FastAPI) for integration and a web interface (Streamlit) for demos.

Applications:

• P2P Markets: Increase trust among users.
• DAOs: Member evaluation, voting, and governance.
• AIRDROPS: Selection of trustworthy users.
• JOBS: Candidate evaluation, demonstration of on-chain experience.

Tech Stack

• Backend: Python, FastAPI
• Blockchain: Solidity, Hardhat
• Blockchain Interaction: Web3.py
• Demo Interface: Streamlit
• Testing Environment: Ganache

Installation and Setup

1. Clone the repository:

   git clone https://github.com/josem-nex/blockchain-reputation.git
   cd blockchain-reputation

2. Create and activate a virtual environment:

   python -m venv venv
   source venv/bin/activate  # macOS/Linux
   # venv\Scripts\activate  # Windows

3. Install the dependencies:

   pip install -r requirements.txt

4. Configure the environment:

   • Rename .env.example to .env and fill in the required values (RPC node URL, contract address, etc.).

5. Run the application:

   streamlit run app.py

Usage

The application can be operated in two ways:

1. Web Interface: Navigate to the localhost address provided by Streamlit to perform manual analyses.
2. RESTful API: Integrate the service into your applications by consuming the /analyze endpoint available at the link provided by the API upon startup.