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Machine learning

Secure and Intelligent Energy Markets: Blockchain-Based Peer-to-Peer Energy Trading Framework

Strategic Insights

— 1 min read

A new study published in the Journal of Internet of Things has proposed a secure and intelligent peer-to-peer energy trading framework called zkPET. The framework integrates machine learning and blockchain with advanced cryptographic techniques to protect user data while enabling intelligent decision-making. According to the researchers, zkPET addresses significant challenges related to privacy, scalability, and the integration of advanced artificial intelligence in blockchain-based peer-to-peer energy trading. Experimental validation using a real-world electricity dataset demonstrates the feasibility and effectiveness of zkPET.

Key Takeaways:

  • The zkPET framework integrates machine learning and blockchain with advanced cryptographic techniques to protect user data while enabling intelligent decision-making.
  • The computationally intensive operations of various machine learning models are executed off-chain, and only succinct cryptographic proofs of these computations are uploaded to the blockchain for verification and recording.
  • A time-series clustering approach is incorporated into federated learning to enhance both inference accuracy and the efficiency of proof generation.
  • Experimental validation using the zero-knowledge proof tool EZKL and a real-world electricity dataset demonstrates the feasibility and effectiveness of zkPET.
  • The research concludes that zkPET has the potential to significantly improve privacy, scalability, and computational efficiency in decentralized energy trading.
  • The study was sponsored by Sui Foundation through their Sui Academic Research Awards, Digital Research Alliance of Canada, and Cybera.
  • The research was conducted by King's University and has been peer-reviewed.

Statistics:

  • The zkPET framework is designed to support decentralized energy trading with improved privacy, scalability, and computational efficiency.
  • The framework integrates machine learning with blockchain and advanced cryptographic techniques to protect user data.
  • Experimental validation of zkPET using a real-world electricity dataset showed improved inference accuracy and efficiency of proof generation.
  • The study was conducted by a team of researchers from King's University, including Caixiang Fan, Amirhossein Sohrabbeig, and Petr Musilek.
  • The research received funding from Sui Foundation, Digital Research Alliance of Canada, and Cybera.

Sources:

  • Zero-knowledge Machine Learning Models for Blockchain Peer-to-peer Energy Trading. Internet of Things, 2025;32. Internet of Things can be contacted at: Elsevier, Radarweg 29, 1043 Nx Amsterdam, Netherlands.
  • NewsRx. New Information and Data Encoding and Encryption Study Findings Have Been Reported by Investigators at King's University (Zero-knowledge Machine Learning Models for Blockchain Peer-to-peer Energy Trading). Information Technology Newsweekly. July 8, 2025; p 717.

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