Intelligent Privacy-Aware Computation Offloading Method against Multi-server Joint Inference Attacks

MIN Minghui; LIU Mingcheng; ZHANG Peng; DUAN Jincheng; LI Shiyin; ZHANG Hongliang

doi:10.11999/JEIT260249

Article Contents

Article Navigation > Journal of Electronics & Information Technology > 2025 >

MIN Minghui, LIU Mingcheng, ZHANG Peng, DUAN Jincheng, LI Shiyin, ZHANG Hongliang. Intelligent Privacy-Aware Computation Offloading Method against Multi-server Joint Inference Attacks[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260249

Citation:

MIN Minghui, LIU Mingcheng, ZHANG Peng, DUAN Jincheng, LI Shiyin, ZHANG Hongliang. Intelligent Privacy-Aware Computation Offloading Method against Multi-server Joint Inference Attacks[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260249

Citation:

MIN Minghui, LIU Mingcheng, ZHANG Peng, DUAN Jincheng, LI Shiyin, ZHANG Hongliang. Intelligent Privacy-Aware Computation Offloading Method against Multi-server Joint Inference Attacks[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260249

PDF( 3489 KB)

Intelligent Privacy-Aware Computation Offloading Method against Multi-server Joint Inference Attacks

doi: 10.11999/JEIT260249 cstr: 32379.14.JEIT260249

1.
School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China
2.
School of Electronics, Peking University, Beijing 100871, China

Funds: The National Natural Science Foundation of China (62571529, U25A20388, 62371451), Jiangsu Province Basic Research Special Funds (Natural Science Foundation) (BK20242083), Jiangsu Province Young Scientific and Technological Talent Support Program, (JSTJ-2024-039), Postgraduate Research \& Practice Innovation Program of Jiangsu Province (KYCX25_2841), Graduate Innovation Program of China University of Mining and Technology (2025WLKXJ104)

Received Date: 2026-03-09
Accepted Date: 2026-06-15
Rev Recd Date: 2026-06-04

Available Online: 2026-06-23

Abstract

Abstract

Objective With the rapid development of the low-altitude economy, services such as intelligent transportation, smart healthcare, and low-altitude logistics have become increasingly common. Their efficient operation depends on the real-time processing of massive sensing data. Mobile Edge Computing (MEC) improves task execution efficiency and reduces device computational burdens by offloading tasks to nearby servers. However, user privacy and security risks have become increasingly severe. In dynamic scenarios where multiple MEC servers jointly process tasks, information sharing can enable multi-server joint inference attacks and greatly increase the risk of user location privacy leakage. Although existing studies have used Differential Privacy (DP) to protect user location privacy, current DP-based solutions remain limited. These methods inject noise into offloading decisions, but unconstrained noise may reduce task allocation accuracy. In addition, user mobility causes continuous changes in channel states during dynamic computation offloading. Privacy leakage risks and attacker behaviors are also uncertain. Traditional optimization methods based on static system models are therefore unsuitable for such dynamic environments. To address these challenges, this paper proposes an Asynchronous Advantage Actor-Critic (A3C)-based Intelligent Privacy-Aware Computation Offloading (AIPCO) scheme against multi-server joint inference attacks. The proposed scheme protects user location privacy while maximizing the overall utility of the MEC system. Methods This paper proposes a DP-based task offloading rate perturbation mechanism. By adding controlled noise, the mechanism increases the randomness of user task offloading toward multiple MEC servers. A truncated Laplace mechanism is used to constrain the perturbed offloading rates within valid boundaries. This design satisfies the mathematical guarantees of DP and reduces the accuracy of multi-server joint inference attacks on sensitive user locations. Privacy entropy is then introduced to dynamically evaluate the real-time privacy protection level. Finally, the AIPCO scheme is constructed. Through a multi-threaded asynchronous training mechanism, the scheme interacts with the environment through iterative trial and error and efficiently learns the optimal real-time offloading policy online. The proposed scheme dynamically protects user privacy, reduces computational cost, and maximizes comprehensive system utility. Results and Discussions The AIPCO scheme jointly optimizes user privacy and task offloading cost by incorporating multidimensional performance variables into the reinforcement learning reward function. A comprehensive performance analysis (Fig. 4) shows that, when the number of continuous learning iterations reaches 200, the privacy protection level of AIPCO increases by 2.52%, 3.56%, and 22.90% compared with RCLM, JODRL, and DODA-DT, respectively. This advantage is mainly attributed to the DP-based task offloading rate perturbation method, which uses the truncated Laplace mechanism to increase data randomness while strictly constraining the perturbation range. By contrast, RCLM perturbs the task offloading rate through range-limited DP without using the truncated Laplace mechanism. JODRL increases randomness only through network policy optimization, resulting in a lower privacy protection level. DODA-DT focuses on balancing energy consumption and system latency without optimizing user privacy. For the privacy weight parameter (Fig. 5), increasing $\omega$ improves privacy protection. For example, the privacy protection level increases by 5.64% when $\omega$ rises from 0.2 to 0.7, with a clear performance gain at 0.7. As the system agent reduces its focus on computational cost, user utility remains optimal despite increased cost. When the physical distance between users and the MEC server is adjusted (Fig. 6), AIPCO shows stronger privacy protection in long-distance scenarios. A greater distance reduces the number of tasks offloaded to the server. Therefore, attackers obtain less information, and privacy protection improves. Although computational cost increases with distance, AIPCO consistently outperforms competing schemes. These results confirm that AIPCO achieves optimal MEC system utility while protecting user privacy. Conclusions To mitigate multi-server joint inference attacks caused by information sharing among collaborative MEC servers, this paper proposes an AIPCO method. A DP-based task offloading rate perturbation scheme is designed to increase randomness, and a truncated Laplace mechanism is used to constrain perturbed rates within reasonable boundaries. The scheme is proven to satisfy strict DP mathematical guarantees, and privacy entropy is introduced to quantitatively evaluate the privacy protection level. In addition, the AIPCO scheme uses a multi-threaded asynchronous training mode, enabling the agent to efficiently learn the optimal perturbed offloading policy in a continuous space and maximize overall system utility. Simulation results show that the proposed scheme outperforms the baselines in both dynamic and average performance. It achieves optimal system utility while protecting user privacy.
- Mobile Edge Computing(MEC),
- Joint inference attacks,
- Location Privacy (LP),
- Differential Privacy (DP),
- Asynchronous Advantage Actor-Critic algorithm (A3C)

FullText(HTML)

References(27)

References

[1]	胡钰林, 喻鑫岚, 高伟, 等. 低时延工业物联网中移动边缘计算的安全性与可靠性联合优化[J]. 电子与信息学报, 2025, 47(10): 3492–3504. doi: 10.11999/JEIT250262. HU Yulin, YU Xinlan, GAO Wei, et al. Security and reliability-optimal offloading for mobile edge computing in low-latency industrial IoT[J]. Journal of Electronics & Information Technology, 2025, 47(10): 3492–3504. doi: 10.11999/JEIT250262.
[2]	YANG Helin, ZHENG Mengting, SHAO Ziling, et al. Intelligent computation offloading and trajectory planning for 3-D target search in low-altitude economy scenarios[J]. IEEE Wireless Communications Letters, 2025, 14(4): 949–953. doi: 10.1109/LWC.2025.3527005.
[3]	施建锋, 陈忻阳, 李宝龙. 面向物联网的云边端协同计算中任务卸载与资源分配算法研究[J]. 电子与信息学报, 2025, 47(2): 458–469. doi: 10.11999/JEIT240659. SHI Jianfeng, CHEN Xinyang, and LI Baolong. Research on task offloading and resource allocation algorithms in cloud-edge-end collaborative computing for the internet of things[J]. Journal of Electronics & Information Technology, 2025, 47(2): 458–469. doi: 10.11999/JEIT240659.
[4]	闵明慧, 张鹏, 朱浩鹏, 等. 能量收集辅助的矿山物联网智能计算卸载方法[J]. 电子与信息学报, 2023, 45(10): 3547–3557. doi: 10.11999/JEIT220973. MIN Minghui, ZHANG Peng, ZHU Haopeng, et al. Energy harvesting assisted intelligent computation offloading method for the IoT in mining[J]. Journal of Electronics & Information Technology, 2023, 45(10): 3547–3557. doi: 10.11999/JEIT220973.
[5]	LAN Wenjun, CHEN Kongyang, LI Yikai, et al. Deep reinforcement learning for privacy-preserving task offloading in integrated satellite-terrestrial networks[J]. IEEE Transactions on Mobile Computing, 2024, 23(10): 9678–9691. doi: 10.1109/TMC.2024.3366928.
[6]	WANG Zhibo, SUN Yunan, LIU Defang, et al. Location privacy-aware task offloading in mobile edge computing[J]. IEEE Transactions on Mobile Computing, 2024, 23(3): 2269–2283. doi: 10.1109/TMC.2023.3254553.
[7]	MIN Minghui, ZHANG Peng, ZHANG Yue, et al. Personalized location privacy-aware task offloading: A dual-agent DRL approach[J]. IEEE Transactions on Mobile Computing, 2026, 25(4): 5824–5838. doi: 10.1109/TMC.2025.3634361.
[8]	HE Yulong, HE Xiaofan, JIN Richeng, et al. Location privacy-aware and energy-efficient offloading for distributed edge computing[J]. IEEE Transactions on Wireless Communications, 2023, 22(11): 7975–7988. doi: 10.1109/TWC.2023.3257197.
[9]	WU Guowen, CHEN Xihang, GAO Zhengjun, et al. Privacy-preserving offloading scheme in multi-access mobile edge computing based on MADRL[J]. Journal of Parallel and Distributed Computing, 2024, 183: 104775. doi: 10.1016/j.jpdc.2023.104775.
[10]	FANG Juan, QU Dezheng, CHEN Huijie, et al. Dependency-aware dynamic task offloading based on deep reinforcement learning in mobile-edge computing[J]. IEEE Transactions on Network and Service Management, 2024, 21(2): 1403–1415. doi: 10.1109/TNSM.2023.3319294.
[11]	ZHANG Degan, AN Hongzhan, ZHANG Jie, et al. Novel privacy awareness task offloading approach based on privacy entropy[J]. IEEE Transactions on Network and Service Management, 2024, 21(3): 3598–3608. doi: 10.1109/TNSM.2024.3355967.
[12]	LI Peisong, XIAO Ziren, GAO Honghao, et al. Reinforcement learning based edge-end collaboration for multi-task scheduling in 6G enabled intelligent autonomous transport systems[J]. IEEE Transactions on Intelligent Transportation Systems, 2025, 26(10): 17624–17637. doi: 10.1109/TITS.2024.3525356.
[13]	FENG Jun, YANG L T, ZHU Qing, et al. Privacy-preserving tensor decomposition over encrypted data in a federated cloud environment[J]. IEEE Transactions on Dependable and Secure Computing, 2020, 17(4): 857–868. doi: 10.1109/TDSC.2018.2881452.
[14]	DU Miao, LIU Yinqiu, ZHAO Changyuan, et al. GDP-UAV: A Gaussian differential privacy-empowered multi-UAV task offloading in low-altitude economy networks[J]. IEEE Transactions on Cognitive Communications and Networking, 2026, 12: 5001–5013. doi: 10.1109/TCCN.2025.3638768.
[15]	MIN Minghui, DUAN Jincheng, LIU Mingcheng, et al. Task offloading with differential privacy in multi-access edge computing: An A3C-based approach[J]. IEEE Transactions on Cognitive Communications and Networking, 2026, 12: 5676–5689. doi: 10.1109/TCCN.2026.3657108.
[16]	ZHU Bincheng, HUANG Liang, CHI Kaikai, et al. Enhancing energy efficiency in wireless-powered MEC systems through lyapunov-guided deep reinforcement learning[J]. IEEE Transactions on Wireless Communications, 2025, 24(9): 7563–7580. doi: 10.1109/TWC.2025.3561167.
[17]	GAN Shuying, SIEW M, XU Chao, et al. Differentially private deep Q-learning for pattern privacy preservation in MEC offloading[C]. 2023 IEEE International Conference on Communications, Rome, Italy, 2023: 3578–3583. doi: 10.1109/ICC45041.2023.10278840.
[18]	PANG Xiaoyi, WANG Zhibo, LI Jingxin, et al. Towards online privacy-preserving computation offloading in mobile edge computing[C]. 2022-IEEE Conference on Computer Communications, London, UK, 2022: 1179–1188. doi: 10.1109/INFOCOM48880.2022.9796748.
[19]	ZHANG Pengfei, SUN Hong, ZHANG Zhikun, et al. Privacy-preserving recommendations with mixture model-based matrix factorization under local differential privacy[J]. IEEE Transactions on Industrial Informatics, 2025, 21(7): 5451–5459. doi: 10.1109/TII.2025.3555993.
[20]	FENG Jun, YANG L T, NIE Xin, et al. Edge-cloud-aided differentially private tucker decomposition for cyber-physical-social systems[J]. IEEE Internet of Things Journal, 2022, 9(11): 8387–8396. doi: 10.1109/JIOT.2020.3004826.
[21]	DEMIR S, STAPPERS B, KOK K, et al. Statistical arbitrage trading on the intraday market using the asynchronous advantage actor–critic method[J]. Applied Energy, 2022, 314: 118912. doi: 10.1016/j.apenergy.2022.118912.
[22]	方贤进, 甄雅茹, 张朋飞, 等. 本地差分隐私下技能感知的任务分配算法研究[J]. 电子与信息学报, 2025, 47(11): 4429–4439. doi: 10.11999/JEIT250425. FANG Xianjin, ZHEN Yaru, ZHANG Pengfei, et al. Research on skill-aware task assignment algorithm under local differential privacy[J]. Journal of Electronics & Information Technology, 2025, 47(11): 4429–4439. doi: 10.11999/JEIT250425.
[23]	CHEN Jiajun, HU Chunqiang, SHENG Weihong, et al. Sensitivity-aware personalized differential privacy guarantees for online social networks[J]. IEEE Transactions on Information Forensics and Security, 2025, 20: 3116–3130. doi: 10.1109/TIFS.2025.3551642.
[24]	杨淼焱, 方旭明. 车联网中采用混合分层DRL的无人机辅助移动边缘计算[J]. 电子与信息学报, 2026, 48(4): 1668–1677. doi: 10.11999/JEIT250743. YANG Miaoyan and FANG Xuming. UAV-assisted mobile edge computing based on hybrid hierarchical DRL in the internet of vehicular[J]. Journal of Electronics & Information Technology, 2026, 48(4): 1668–1677. doi: 10.11999/JEIT250743.
[25]	HUSSAIN A, AKBAR W, HUSSAIN T, et al. Ensuring zero trust IoT data privacy: Differential privacy in blockchain using federated learning[J]. IEEE Transactions on Consumer Electronics, 2025, 71(1): 1167–1179. doi: 10.1109/TCE.2024.3444824.
[26]	FENG Libo, GUO Junwei, FANG Fake, et al. SEPP-FLBC: A secure and efficient privacy protection scheme using federate learning and blockchain for edge-end-cloud devices[J]. IEEE Transactions on Services Computing, 2026, 19(1): 657–670. doi: 10.1109/TSC.2025.3641964.
[27]	GAN Ying, XIAO Zhanhui, KE Ting, et al. UAV application with XAI and soft computing methods to protect privacy of power-data in smart grids[J]. IEEE Transactions on Consumer Electronics, 2025, 71(2): 3921–3929. doi: 10.1109/TCE.2025.3551519.