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Data Privacy Preservation and Security in Smart Metering Systems

Author

Listed:
  • Mohamed S. Abdalzaher

    (Department of Seismology, National Research Institute of Astronomy and Geophysics, Cairo 11421, Egypt)

  • Mostafa M. Fouda

    (Department of Electrical and Computer Engineering, College of Science and Engineering, Idaho State University, Pocatello, ID 83209, USA)

  • Mohamed I. Ibrahem

    (Department of Cyber Security Engineering, George Mason University, Fairfax, VA 22030, USA
    Department of Electrical Engineering, Faculty of Engineering at Shoubra, Benha University, Cairo 11672, Egypt)

Abstract

Smart meters (SMs) can play a key role in monitoring vital aspects of different applications such as smart grids (SG), alternative currents (AC) optimal power flows, adversarial training, time series data, etc. Several practical privacy implementations of SM have been made in the literature, but more studies and testing may be able to further improve efficiency and lower implementation costs. The major objectives of cyberattacks are the loss of data privacy on SM-based SG/power grid (PG) networks and threatening human life. As a result, losing data privacy is very expensive and gradually hurts the national economy. Consequently, employing an efficient trust model against cyberattacks is strictly desired. This paper presents a research pivot for researchers who are interested in security and privacy and shade light on the importance of the SM. We highlight the involved SMs’ features in several applications. Afterward, we focus on the SMs’ vulnerabilities. Then, we consider eleven trust models employed for SM security, which are among the common methodologies utilized for attaining and preserving the data privacy of the data observed by the SMs. Following that, we propose a comparison of the existing solutions for SMs’ data privacy. In addition, valuable recommendations are introduced for the interested scholars, taking into consideration the vital effect of SM protection on disaster management, whether on the level of human lives or the infrastructure level.

Suggested Citation

  • Mohamed S. Abdalzaher & Mostafa M. Fouda & Mohamed I. Ibrahem, 2022. "Data Privacy Preservation and Security in Smart Metering Systems," Energies, MDPI, vol. 15(19), pages 1-19, October.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:19:p:7419-:d:937645
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    References listed on IDEAS

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    12. Omar Hamdy & Hanan Gaber & Mohamed S. Abdalzaher & Mahmoud Elhadidy, 2022. "Identifying Exposure of Urban Area to Certain Seismic Hazard Using Machine Learning and GIS: A Case Study of Greater Cairo," Sustainability, MDPI, vol. 14(17), pages 1-24, August.
    13. Christos Athanasiadis & Dimitrios Doukas & Theofilos Papadopoulos & Antonios Chrysopoulos, 2021. "A Scalable Real-Time Non-Intrusive Load Monitoring System for the Estimation of Household Appliance Power Consumption," Energies, MDPI, vol. 14(3), pages 1-23, February.
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    15. Zeeshan Aslam & Nadeem Javaid & Ashfaq Ahmad & Abrar Ahmed & Sardar Muhammad Gulfam, 2020. "A Combined Deep Learning and Ensemble Learning Methodology to Avoid Electricity Theft in Smart Grids," Energies, MDPI, vol. 13(21), pages 1-24, October.
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    Cited by:

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    2. Mohamed S. Abdalzaher & Mostafa M. Fouda & Ahmed Emran & Zubair Md Fadlullah & Mohamed I. Ibrahem, 2023. "A Survey on Key Management and Authentication Approaches in Smart Metering Systems," Energies, MDPI, vol. 16(5), pages 1-27, March.
    3. Chankook Park & Wan Gyu Heo & Myung Eun Lee, 2024. "Study on Consumers’ Perceived Benefits and Risks of Smart Energy System," International Journal of Energy Economics and Policy, Econjournals, vol. 14(3), pages 288-300, May.
    4. Mahmoud M. Badr & Mohamed I. Ibrahem & Hisham A. Kholidy & Mostafa M. Fouda & Muhammad Ismail, 2023. "Review of the Data-Driven Methods for Electricity Fraud Detection in Smart Metering Systems," Energies, MDPI, vol. 16(6), pages 1-18, March.
    5. Mohamed S. Abdalzaher & Moez Krichen & Derya Yiltas-Kaplan & Imed Ben Dhaou & Wilfried Yves Hamilton Adoni, 2023. "Early Detection of Earthquakes Using IoT and Cloud Infrastructure: A Survey," Sustainability, MDPI, vol. 15(15), pages 1-38, July.
    6. Luigi Rubino & Guido Rubino & Raffaele Esempio, 2023. "Linear Programming-Based Power Management for a Multi-Feeder Ultra-Fast DC Charging Station," Energies, MDPI, vol. 16(3), pages 1-17, January.
    7. Rasheed Abdulkader & Hayder M. A. Ghanimi & Pankaj Dadheech & Meshal Alharbi & Walid El-Shafai & Mostafa M. Fouda & Moustafa H. Aly & Dhivya Swaminathan & Sudhakar Sengan, 2023. "Soft Computing in Smart Grid with Decentralized Generation and Renewable Energy Storage System Planning," Energies, MDPI, vol. 16(6), pages 1-24, March.
    8. Mohamed S. Abdalzaher & Hussein A. Elsayed & Mostafa M. Fouda & Mahmoud M. Salim, 2023. "Employing Machine Learning and IoT for Earthquake Early Warning System in Smart Cities," Energies, MDPI, vol. 16(1), pages 1-22, January.
    9. Hany Habbak & Mohamed Mahmoud & Khaled Metwally & Mostafa M. Fouda & Mohamed I. Ibrahem, 2023. "Load Forecasting Techniques and Their Applications in Smart Grids," Energies, MDPI, vol. 16(3), pages 1-33, February.

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