A privacy-preserving-framework-based blockchain and deep learning for protecting smart power networks

Marwa Keshk, Benjamin Turnbull, Nour Moustafa, Dinusha Vatsalan, Kim-Kwang Raymond Choo*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

119 Citations (Scopus)


Modern power systems depend on cyber-physical systems to link physical devices and control technologies. A major concern in the implementation of smart power networks is to minimize the risk of data privacy violation (e.g., by adversaries using data poisoning and inference attacks). In this article, we propose a privacy-preserving framework to achieve both privacy and security in smart power networks. The framework includes two main modules: A two-level privacy module and an anomaly detection module. In the two-level privacy module, an enhanced-proof-of-work-Technique-based blockchain is designed to verify data integrity and mitigate data poisoning attacks, and a variational autoencoder is simultaneously applied for transforming data into an encoded format for preventing inference attacks. In the anomaly detection module, a long short-Term memory deep learning technique is used for training and validating the outputs of the two-level privacy module using two public datasets. The results highlight that the proposed framework can efficiently protect data of smart power networks and discover abnormal behaviors, in comparison to several state-of-The-Art techniques.

Original languageEnglish
Pages (from-to)5110-5118
Number of pages9
JournalIEEE Transactions on Industrial Informatics
Issue number8
Early online date2 Dec 2019
Publication statusPublished - Aug 2020
Externally publishedYes


  • Anomaly detection
  • blockchain
  • cyber-physical system (CPS)
  • deep learning
  • privacy preservation
  • proof of work (PoW)


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