Privacy-Preserving Machine Learning for IoT-Integrated Smart Grids: Recent Advances, Opportunities, and Challenges

Mazhar Ali, Moharana Suchismita, Syed Saqib Ali and Bong Jun Choi ()
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Mazhar Ali: School of Computer Science and Engineering, Soongsil University, Seoul 06978, Republic of Korea
Moharana Suchismita: School of Computer Science and Engineering, Soongsil University, Seoul 06978, Republic of Korea
Syed Saqib Ali: School of Computer Science and Engineering, Soongsil University, Seoul 06978, Republic of Korea
Bong Jun Choi: School of Computer Science and Engineering, Soongsil University, Seoul 06978, Republic of Korea

Energies, 2025, vol. 18, issue 10, 1-31

Abstract: Ensuring the safe, reliable, and energy-efficient provision of electricity is a complex task for smart grid (SG) management applications. Internet of Things (IoT) and edge computing-based SG applications have been proposed for time-responsive monitoring and controlling tasks related to power systems. Recent studies have provided valuable insights into the potential of machine learning algorithms in SGs, covering areas such as generation, distribution, microgrids, consumer energy market, and cyber security. Integrated IoT devices directly exchange data with the SG cloud, which increases the vulnerability and security threats to the energy system. The review aims to provide a comprehensive analysis of privacy-preserving machine learning (PPML) applications in IoT-Integrated SGs, focusing on non-intrusive load monitoring, fault detection, demand forecasting, generation forecasting, energy-management systems, anomaly detection, and energy trading. The study also highlights the importance of data privacy and security when integrating these applications to enable intelligent decision-making in smart grid domains. Furthermore, the review addresses performance issues (e.g., accuracy, latency, and resource constraints) associated with PPML techniques, which may impact the security and overall performance of IoT-integrated SGs. The insights of this study will provide essential guidelines for in-depth research in the field of IoT-integrated smart grid privacy and security in the future.

Keywords: smart grids (SGs); Internet of Things (IoT); privacy-preserving machine learning (PPML); cybersecurity; privacy; security; cybersecurity threats; attacks; cyber–physical systems (CPSs); differential privacy (DP); multiparty computation (MPC); homomorphic encryption (HE); federated learning (FL); trusted execution environment (TEE); zero-knowledge proof (ZKP) (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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