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Lookup NU author(s): Yizhou Shen
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
The emergence of mobile edge computing (MEC) imposes an unprecedented pressure on privacy protection, although it helps the improvement of computation performance including energy consumption and computation delay by computation offloading. To this end, we concern about the privacy protection in the MEC system with a curious edge server. We present a deep reinforcement learning (DRL)-driven computation offloading strategy designed to concurrently optimize privacy protection and computation cost. We investigate the potential privacy breaches resulting from offloading patterns, propose an attack model of privacy theft, and correspondingly define an analytical measure to assess privacy protection levels. In pursuit of an ideal computation offloading approach, we propose an algorithm, SAC-PP, which integrates actor-critic, off-policy, and maximum entropy to improve the efficiency of learning processes. We explore the sensitivity of SAC-PP to hyperparameters and the results demonstrate its stability, which facilitates application and deployment in real environments. The relationship between privacy protection and computation cost is analyzed with different reward factors. Compared with benchmarks, the empirical results from simulations illustrate that the proposed computation offloading approach exhibits enhanced learning speed and overall performance.
Author(s): Shen S, Hao X, Gao Z, Wu G, Shen Y, Zhang H, Cao Q, Yu S
Publication type: Article
Publication status: Published
Journal: IEEE Transactions on Network and Service Management
Year: 2024
Pages: ePub ahead of Print
Online publication date: 22/08/2024
Acceptance date: 02/04/2018
Date deposited: 03/09/2024
ISSN (electronic): 1932-4537
Publisher: IEEE
URL: https://doi.org/10.1109/TNSM.2024.3447753
DOI: 10.1109/TNSM.2024.3447753
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