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Privacy-Preserving Distributed Analytics in Fog-Enabled IoT Systems.

Liang Zhao1

  • 1Department of Information Technology, Kennesaw State University, Marietta, GA 30060, USA.

Sensors (Basel, Switzerland)
|November 3, 2020
PubMed
Summary
This summary is machine-generated.

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Development of a Noninvasive Blood Glucose Monitoring System Prototype: Pilot Study.

JMIR formative research·2022
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This study introduces a distributed analytics framework for fog-enabled Internet of Things (IoT) systems. It enhances data privacy and reduces latency for faster, more efficient IoT data analysis.

Area of Science:

  • Computer Science
  • Data Science
  • Network Engineering

Background:

  • Internet of Things (IoT) data analytics face challenges with increasing data volumes and latency limitations of cloud-based solutions.
  • Traditional cloud solutions struggle with bandwidth and response time requirements for time-sensitive IoT applications.
  • Edge and fog computing offer potential solutions for distributed data processing in IoT.

Purpose of the Study:

  • To develop a distributed analytics framework for fog-enabled IoT systems to reduce latency and avoid raw data movement.
  • To propose a privacy-preserving protocol using cryptographic schemes to protect data holders' information.
  • To evaluate the framework's performance and accuracy in real-world case studies.

Main Methods:

  • A distributed analytics framework leveraging computational capacities of edge devices and fog nodes.
Keywords:
distributed analyticsfog computinginternet of thingsprivacy-preserving

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  • Implementation of a privacy-preserving protocol with cryptographic schemes for enhanced data security.
  • Experimental validation on seismic imaging, diabetes progression prediction, and Enron email classification datasets.

    • Main Results:

    • The proposed framework avoids raw data movement and significantly reduces latency in fog-enabled IoT systems.
    • The privacy-preserving protocol ensures that raw data cannot be inferred by honest-but-curious neighbors without affecting solution accuracy.
    • Experiments showed the algorithm to be up to one order of magnitude faster for seismic imaging compared to benchmarks.

    Conclusions:

    • The developed distributed analytics framework is effective for fog-enabled IoT systems, offering reduced latency and enhanced privacy.
    • The privacy-preserving protocol maintains solution accuracy while safeguarding sensitive edge device data.
    • The methodology demonstrates significant potential as a promising solution for efficient and secure IoT data analytics.