Zero-Knowledge Machine Learning Models for Blockchain Peer-to-Peer Energy Trading

This project aims to research, design and evaluate zero-knwledge machine learning (ZKML) models for blockchain-based decentralized energy trading. The inherent transparency of blockchain raises concerns about user privacy, which can lead to sensitive energy data leakage. Additionally, the cost of on-chain computation and the limitations of smart contracts hinder the integration of advanced artificial intelligence technologies. In this project, we propose to leverage blockchain and ZKML technologies to design a novel privacy-preserving and intelligent solution for peer-to-peer energy trading. This solution contains three potential models (or structures), i.e., centralized, decentralized, and federated ZKML from the architectural perspective.