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IoT-Enabled Fog-Based Secure Aggregation in Smart Grids Supporting Data Analytics.

Hayat Mohammad Khan1, Farhana Jabeen1, Abid Khan2

  • 1Department of Computer Science, COMSATS University, Islamabad 45550, Pakistan.

Sensors (Basel, Switzerland)
|October 16, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a secure data analytics scheme for smart grids, enhancing privacy and efficiency. The Fog-enabled Secure Data Analytics Operations (FESDAO) scheme ensures data integrity and resilience against attacks.

Keywords:
ANOVABGNIoTsdata analyticsfault-tolerancefog computinghomomorphic encryptionprivacy preservationsmart gridstatistical analysis

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Cybersecurity

Background:

  • The Internet of Things (IoT) offers automation and efficiency, with smart grids benefiting from IoT and data analytics for improved energy management.
  • Data analytics, particularly statistical analytics, is crucial for extracting insights from smart grid data, but privacy and security are major concerns.
  • Current smart grid systems face challenges in securely aggregating and analyzing data while maintaining privacy and resilience.

Purpose of the Study:

  • To propose a secure, privacy-aware data aggregation scheme for smart grids.
  • To enhance the capabilities of data analytics in smart grids through a distributed architecture.
  • To address security vulnerabilities including insider threats, false data injection, and replay attacks.

Main Methods:

  • Introduction of the Fog-enabled Secure Data Analytics Operations (FESDAO) scheme, a distributed architecture.
  • Implementation of a modified Boneh-Goh-Nissim cryptographic scheme for privacy-preserving data aggregation.
  • Integration of secure aggregation, authentication, fault tolerance, and resilience mechanisms.
  • Support for statistical analytics at both cloud and fog node levels.

Main Results:

  • FESDAO provides robust security features, including secure aggregation, authentication, and fault tolerance.
  • The scheme ensures data privacy during aggregation and supports accurate statistical analytics.
  • FESDAO demonstrates resilience against insider threats, false data injection, and replay attacks, even with meter failures.
  • Performance evaluations show FESDAO is efficient compared to existing schemes in terms of various costs.

Conclusions:

  • The proposed FESDAO scheme effectively enhances security and privacy in smart grid data analytics.
  • It offers a reliable and resilient solution for data aggregation and analysis in smart grids.
  • FESDAO contributes significantly to the secure integration of IoT and data analytics in the energy sector.