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Privacy-Preserving Task Offloading Strategies in MEC.

Haijian Yu1,2, Jing Liu1,2, Chunjie Hu1,2

  • 1College of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan 430065, China.

Sensors (Basel, Switzerland)
|January 8, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a privacy-preserving task offloading scheme for mobile edge computing (MEC). It protects user location and association privacy while minimizing task completion time using proxy forwarding and intelligent server selection.

Keywords:
association privacylocation privacymobile edge computingprivacy protectiontask offloading

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Area of Science:

  • Mobile Edge Computing (MEC)
  • Cybersecurity
  • Wireless Communication

Background:

  • Mobile devices offload tasks to edge servers in MEC to reduce completion time and energy consumption.
  • Wireless data transfer in MEC poses privacy risks, particularly location and association privacy leakage.
  • Protecting mobile device privacy during task offloading is crucial.

Purpose of the Study:

  • To design a privacy-preserving task offloading scheme for MEC.
  • To simultaneously protect location and association privacy.
  • To minimize average task completion time.

Main Methods:

  • Implemented a proxy forwarding mechanism to safeguard location privacy.
  • Developed a proxy server selection policy based on location information.
  • Designed an edge server selection strategy considering task privacy conflicts, computing power, and location.

Main Results:

  • The proposed scheme effectively protects both location and association privacy.
  • The scheme significantly reduces the average task completion time.
  • Experimental results show superior performance compared to existing techniques.

Conclusions:

  • The developed scheme offers a robust solution for privacy preservation in MEC task offloading.
  • It balances privacy protection with efficient task execution.
  • This approach enhances user security in mobile edge environments.