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Frontiers of Information Technology & Electronic Engineering
ISSN 2095-9184 (print), ISSN 2095-9230 (online)
2025 Vol.26 No.1 P.27-41
PPDO: a privacy-preservation-aware delay optimization task-offloading algorithm for collaborative edge computing
Abstract: Although collaborative edge computing (CEC) systems are beneficial in enhancing the performance of mobile edge computing (MEC), the issue of user privacy leakage becomes prominent during task offloading. To address this issue, we design a privacy-preservation-aware delay optimization task-offloading algorithm (PPDO) in a CEC system. By considering location and usage pattern privacy protection, we establish a privacy task model to interfere with the edge server and ensure user privacy. To address the extra delay arising from privacy protection, we subsequently leverage a Markov decision processing (MDP) policy-iteration-based algorithm to minimize delays without compromising privacy. To simultaneously accelerate the MDP operation, we develop an extension that improves the PPDO by optimizing the action set. Finally, a comprehensive simulation was conducted using the edge user allocation (EUA) dataset. The results demonstrated that PPDO achieves an optimal trade-off between privacy protection and delay with a minimum delay compared with existing algorithms. Moreover, we examined the advantages and disadvantages of improving PPDO.
Key words: Collaborative edge computing; Task offloading; Privacy protection; Markov decision process
1桂林理工大学计算机科学与工程学院,中国桂林市,541004
2广西嵌入式技术与智能系统重点实验室,中国桂林市,541004
摘要:虽然协同边缘计算系统有利于提高移动边缘计算性能,但是在任务卸载过程中,用户面临严重的隐私泄露问题。为解决这一问题,我们在协同边缘计算系统中设计了一种隐私保护感知时延优化任务卸载算法(PPDO)。通过考虑位置和使用模式两种隐私,建立了一种隐私任务模型来干扰边缘服务器以保护用户隐私。为解决隐私保护带来的额外时延问题,采用基于马尔可夫决策过程的策略迭代算法实现在保护隐私的同时尽量减少时延。同时,为加快马尔可夫决策过程的求解,通过优化动作集改进PPDO。最后,采用EUA数据集进行仿真实验。结果表明,与现有算法相比,PPDO以最小延迟实现了隐私保护和时延优化之间的最佳权衡。此外,研究了改进后PPDO算法的优缺点。
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DOI:
10.1631/FITEE.2300741
CLC number:
TP393
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On-line Access:
2025-02-10
Received:
2023-10-31
Revision Accepted:
2024-01-05
Crosschecked:
2025-02-18