A joint privacy protection algorithm for edge computing task offloading based on Dempster–Shafer evidence theory
Edge computing reduces system latency by offloading tasks from cloud servers to Internet of Things (IoT) devices. This architecture decreases network congestion, improves system efficiency, and enables real-time processing. However, in consideration of resource efficiency, user devices tend to selec...
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| Published in | Physical communication Vol. 72; p. 102813 |
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| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier B.V
01.10.2025
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1874-4907 |
| DOI | 10.1016/j.phycom.2025.102813 |
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| Abstract | Edge computing reduces system latency by offloading tasks from cloud servers to Internet of Things (IoT) devices. This architecture decreases network congestion, improves system efficiency, and enables real-time processing. However, in consideration of resource efficiency, user devices tend to select the nearest cloud servers, which may enable potential malicious eavesdropping devices to infer user location through intercepted communication information. Existing research has used privacy entropy to measure user location privacy, and assigned different privacy entropy values to various types of cloud servers. However, user privacy protection in real-world scenarios depends on multiple factors, not just the classification of edge cloud servers. To solve this problem, we propose a joint privacy protection algorithm (JPPA), which introduces a novel metric for user location privacy guarantee called belief, this metric combines two key factors: task caching status and the geographical location of edge cloud servers. We formulate the problem as a constrained Markov decision process (CMDP), aiming to minimize the average network traffic subject to constraints on belief levels and energy outage probability. By transforming the CMDP into a linear programming problem, computational feasibility is ensured. Simulation results demonstrate that compared to traditional privacy entropy-constrained methods, JPPA reduces average network traffic overhead more effectively while ensuring user location privacy. |
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| AbstractList | Edge computing reduces system latency by offloading tasks from cloud servers to Internet of Things (IoT) devices. This architecture decreases network congestion, improves system efficiency, and enables real-time processing. However, in consideration of resource efficiency, user devices tend to select the nearest cloud servers, which may enable potential malicious eavesdropping devices to infer user location through intercepted communication information. Existing research has used privacy entropy to measure user location privacy, and assigned different privacy entropy values to various types of cloud servers. However, user privacy protection in real-world scenarios depends on multiple factors, not just the classification of edge cloud servers. To solve this problem, we propose a joint privacy protection algorithm (JPPA), which introduces a novel metric for user location privacy guarantee called belief, this metric combines two key factors: task caching status and the geographical location of edge cloud servers. We formulate the problem as a constrained Markov decision process (CMDP), aiming to minimize the average network traffic subject to constraints on belief levels and energy outage probability. By transforming the CMDP into a linear programming problem, computational feasibility is ensured. Simulation results demonstrate that compared to traditional privacy entropy-constrained methods, JPPA reduces average network traffic overhead more effectively while ensuring user location privacy. |
| ArticleNumber | 102813 |
| Author | Hao, Wanming Yang, Wencong Yang, Shouyi Chen, Tian Sun, Xuandi Tang, Zhiqing |
| Author_xml | – sequence: 1 givenname: Tian surname: Chen fullname: Chen, Tian email: tianchen@gs.zzu.edu.cn organization: School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou, 450001, Henan, China – sequence: 2 givenname: Wanming orcidid: 0000-0002-4465-3447 surname: Hao fullname: Hao, Wanming email: iewmhao@zzu.edu.cn organization: School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou, 450001, Henan, China – sequence: 3 givenname: Wencong surname: Yang fullname: Yang, Wencong email: yangwencong@gs.zzu.edu.cn organization: School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou, 450001, Henan, China – sequence: 4 givenname: Shouyi orcidid: 0000-0002-5149-5280 surname: Yang fullname: Yang, Shouyi email: iesyyang@zzu.edu.cn organization: School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou, 450001, Henan, China – sequence: 5 givenname: Xuandi surname: Sun fullname: Sun, Xuandi email: xuandisun@mail.nwpu.edu.cn organization: School of Cyber Science and Engineering, Zhengzhou University, Zhengzhou, 450002, Henan, China – sequence: 6 givenname: Zhiqing surname: Tang fullname: Tang, Zhiqing email: iezqtang@zzu.edu.cn organization: School of Cyber Science and Engineering, Zhengzhou University, Zhengzhou, 450002, Henan, China |
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| Cites_doi | 10.1109/ACCESS.2020.2991734 10.1109/TWC.2019.2958091 10.1109/IC_ASET49463.2020.9318269 10.1109/JSAC.2024.3459037 10.1109/TMC.2023.3239339 10.1109/MCOM.2016.7537180 10.1109/ICECCE.2014.7086627 10.1109/ACCESS.2019.2947306 10.1109/JIOT.2020.3039359 10.1109/ACCESS.2022.3224032 10.1109/JIOT.2016.2579198 10.1109/JIOT.2018.2878718 10.1109/JIOT.2018.2875241 10.1109/TNSM.2024.3355967 10.1109/TIFS.2025.3570202 10.1109/TIT.2008.921908 10.1109/JIOT.2023.3239944 10.1109/TNSM.2020.3010967 10.1109/JIOT.2019.2944007 10.1109/JSAC.2023.3240707 10.1109/ACCESS.2020.2970118 10.1109/JIOT.2019.2948888 10.1109/MCOM.2015.7321985 10.1109/COMST.2020.2973314 10.1109/TCC.2020.3030817 10.1109/TITS.2020.2995622 10.1109/ICC51166.2024.10622651 10.1109/JIOT.2018.2805263 10.1109/ICC.2019.8761370 10.1109/GLOCOM.2017.8253985 10.1109/TMC.2023.3298888 |
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| Title | A joint privacy protection algorithm for edge computing task offloading based on Dempster–Shafer evidence theory |
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