Privacy-Preserving Schemes for Safeguarding Heterogeneous Data Sources in Cyber-Physical Systems

Marwa Keshk, Benjamin Turnbull, Elena Sitnikova, Dinusha Vatsalan, Nour Moustafa

Research output: Contribution to journalArticlepeer-review

23 Citations (Scopus)
20 Downloads (Pure)


Cyber-Physical Systems (CPS) underpin global critical infrastructure, including power, water, gas systems and smart grids. CPS, as a technology platform, is unique as a target for Advanced Persistent Threats (APTs), given the potentially high impact of a successful breach. Additionally, CPSs are targets as they produce significant amounts of heterogeneous data from the multitude of devices and networks included in their architecture. It is, therefore, essential to develop efficient privacy-preserving techniques for safeguarding system data from cyber attacks. This paper introduces a comprehensive review of the current privacy-preserving techniques for protecting CPS systems and their data from cyber attacks. Concepts of Privacy preservation and CPSs are discussed, demonstrating CPSs' components and the way these systems could be exploited by either cyber and physical hacking scenarios. Then, classification of privacy preservation according to the way they would be protected, including perturbation, authentication, machine learning (ML), cryptography and blockchain, are explained to illustrate how they would be employed for data privacy preservation. Finally, we show existing challenges, solutions and future research directions of privacy preservation in CPSs.

Original languageEnglish
Pages (from-to)55077-55097
Number of pages21
JournalIEEE Access
Publication statusPublished - 30 Mar 2021
Externally publishedYes


  • authentication
  • blockchain
  • cryptography
  • cyber-physical systems
  • machine learning
  • perturbation
  • Privacy preservation


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