抗多服务器联合推断攻击的智能隐私感知计算卸载方法

闵明慧; 刘明诚; 张鹏; 段金成; 李世银; 张泓亮

doi:10.11999/JEIT260249

抗多服务器联合推断攻击的智能隐私感知计算卸载方法

doi: 10.11999/JEIT260249 cstr: 32379.14.JEIT260249

1.
中国矿业大学信息与控制工程学院徐州 221116
2.
北京大学电子学院北京 100871

基金项目: 国家自然科学基金 (62571529, U25A20388, 62371451)，江苏省基础研究专项资金(自然科学基金)(BK20242083)，江苏省青年科技人才托举工程(JSTJ-2024-039)，江苏省研究生科研与实践创新计划(KYCX25_2841)、中国矿业大学研究生创新专项项目(项目编号：2025WLKXJ104)

详细信息

作者简介:
闵明慧：女，副教授，研究方向为网络安全、边缘计算、隐私保护等

刘明诚：男，硕士生，研究方向为边缘计算、隐私保护等

张鹏：男，博士生，研究方向为边缘计算、隐私保护等

段金成：男，硕士生，研究方向为边缘计算、隐私保护等

李世银：男，教授，研究方向为智能感知与精确定位、智慧物联网等

张泓亮：男，研究员，研究方向为无线通信、智能超表面等

通讯作者:
李世银　lishiyin@cumt.edu.cn

中图分类号: TN929.5
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- 被引次数: 0
出版历程
- 收稿日期: 2026-03-09
- 修回日期: 2026-06-15
- 录用日期: 2026-06-15
- 网络出版日期: 2026-06-23

Intelligent Privacy-Aware Computation Offloading Method Against Multi-Server Joint Inference Attacks

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: 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 under Grant KYCX25_2841、Graduate Innovation Program of China University of Mining and Technology under Grant 2025WLKXJ104

摘要

摘要: 物联网(IoT)设备通过移动边缘计算(MEC)技术将任务卸载到附近的MEC服务器以降低处理能耗和时延，多个MEC服务器在辅助单个移动用户处理计算任务时可通过共享信息实施联合推断攻击，带来更加严重的位置隐私泄露风险。因此，该文提出一种抗多服务器联合推断攻击的智能隐私感知计算卸载方法，构建一种基于差分隐私(DP)的任务卸载率扰动方案，通过增加卸载到不同MEC服务器任务量的随机性，实现保护用户位置隐私，同时使用隐私熵评估隐私保护程度；设计截断拉普拉斯机制约束扰动范围并证明其满足DP。此外，为了在隐私感知的计算卸载动态场景中实现系统效益最大化，提出一种基于异步优势演员-评论家(A3C)算法的抗多服务器联合推断攻击的智能隐私感知计算卸载(AIPCO)方案，利用多线程异步训练机制高效获取最优卸载决策。仿真结果表明所提方案相较于基准方案能够保障位置隐私，获得较高的系统效益。
- 移动边缘计算 /
- 联合推断攻击 /
- 位置隐私 /
- 差分隐私 /
- 异步优势演员-评论家算法
Abstract: Objective With the rapid advancement of the low-altitude economy, services such as intelligent transportation, smart healthcare, and low-altitude logistics have become increasingly pervasive, the efficient operation of which relies heavily on the real-time processing of massive sensor data. Mobile edge computing (MEC) enhances task execution efficiency and alleviates device computational burdens by offloading tasks to nearby MEC servers. However, user privacy and security threats have become progressively severe. In dynamic scenarios where multiple MEC servers collaboratively process tasks, joint inference attacks via information sharing drastically escalate the risk of user location privacy leakage. Although existing studies have adopted differential privacy (DP) to safeguard user location privacy, DP-based solutions remain insufficient. Existing methods inject noise into offloading decisions to protect privacy, yet unconstrained noise can degrade the accuracy of task allocation. Furthermore, in dynamic computation offloading scenarios, the real-time mobility of users induces continuous dynamics in channel states. Both the privacy leakage risks inherent in task offloading and the behaviors of attackers exhibit significant uncertainties. Traditional optimization theories, relying on static system models, fail to cope with the optimization challenges in such dynamic environments. To overcome these challenges, this paper proposes an Asynchronous Advantage Actor-Critic (A3C)-based intelligent privacy-aware computation offloading (AIPCO) scheme capable of resisting multi-server joint inference attacks. While effectively safeguarding user location privacy, the proposed scheme maximizes the overall utility of the MEC system. Methods This paper proposes a DP-based task offloading rate perturbation mechanism. By introducing controlled noise, the mechanism enhances the randomness of user task offloading toward multiple MEC servers. Concurrently, a truncated Laplace mechanism is utilized to constrain the boundaries of the perturbed offloading rates, thereby strictly satisfying the mathematical guarantees of DP and effectively degrading the accuracy of multi-server cooperative inference attacks in identifying sensitive user locations. On this basis, privacy entropy is introduced to dynamically evaluate the real-time efficacy of privacy protection. Finally, the AIPCO scheme is constructed. Leveraging its multi-threaded asynchronous training mechanism, the scheme interacts with the environment through iterative trial-and-error to efficiently learn the optimal real-time offloading policy online. While dynamically safeguarding user privacy, the proposed scheme minimizes computational overhead, ultimately achieving the maximization of the comprehensive system utility. Results and Discussions The AIPCO scheme achieves simultaneous optimization of user privacy and task offloading costs by strategically incorporating multidimensional performance variables into the reward function of reinforcement learning. A comprehensive multi-dimensional performance analysis of this scheme (Figure 4) indicates that, in terms of dynamic convergence performance, when the continuous learning iterations reach 200, its privacy protection level improves significantly by 2.52%, 3.56%, and 22.90% compared to baseline schemes RCLM, JODRL, and DODA-DT, respectively. This distinct advantage stems directly from AIPCO’s adoption of a DP-based method for perturbing the offloading rate, which successfully utilizes a truncated Laplace mechanism to enhance data randomness while strictly limiting the perturbation range. In sharp contrast, RCLM only perturbs the rate via range-limited DP without implementing a truncated Laplace mechanism; JODRL merely increases randomness through network policy optimization, resulting in lower protection levels; and DODA-DT focuses exclusively on balancing energy consumption and system latency without optimizing user privacy. Regarding the critical privacy weight parameters (Figure 5), systematically increasing $w$ enhances privacy protection. For instance, the privacy protection level improves by 5.64% as the weight rises from 0.2 to 0.7, with the performance gain being particularly significant at a weight of 0.7. As the system proxy reduces its primary focus on computational costs, user benefits remain optimal despite rising expenses. Furthermore, when adjusting the physical distance between users and the MEC server (Figure 6), AIPCO demonstrates superior privacy protection capabilities in long-distance scenarios. Greater distance inherently reduces tasks offloaded to the server; thus, the less information an attacker obtains, the better the privacy protection. Although computational costs inevitably rise with distance, AIPCO consistently outperforms competing schemes, confirming that it achieves optimal benefits for the MEC system while safeguarding user privacy. Conclusions To mitigate joint inference attacks from information sharing among collaborative MEC servers, this paper proposes an intelligent privacy-aware computation offloading method. A DP-based task offloading rate perturbation scheme enhances randomness, using a truncated Laplace mechanism to constrain perturbed rates within reasonable boundaries. While proving the scheme strictly satisfies DP mathematical guarantees, privacy entropy is introduced to quantitatively evaluate privacy protection efficacy. Furthermore, the designed AIPCO scheme leverages a multi-threaded asynchronous training mode, enabling the agent to efficiently learn the optimal perturbed offloading policy within a continuous space to maximize overall system utility. Simulation results demonstrate that the proposed scheme significantly outperforms baselines in both dynamic and average performance, achieving optimal system utility while safeguarding user privacy.
- Mobile Edge Computing /
- Joint Inference Attacks /
- Location Privacy /
- Differential Privacy /
- Asynchronous Advantage Actor-Critic Algorithm

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图 1 N个MEC服务器服务单个移动用户的隐私感知计算卸载模型

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图 2 多MEC服务器服务单个移动用户场景下的隐私泄露分析

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图 3 基于A3C的抗多服务器联合推断攻击的智能隐私感知计算卸载方案

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图 4 不同隐私保护方案下的动态性能分析

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图 5 不同隐私保护方案下隐私权重$ \omega $对系统性能的影响

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图 6 不同隐私保护方案下随着用户位置相对于MEC服务器位置变化的平均性能

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1 基于A3C的抗多服务器联合推断攻击的智能隐私感知计算卸载方案

输入：状态$ {\boldsymbol{s}}^{(k)} $
输出：策略分布函数$ \pi ({\boldsymbol{s}}^{(k)},{\boldsymbol{a}}^{(k)}) $和状态值$ V({\boldsymbol{s}}^{(k)}) $
1: 初始化MEC系统的坐标信息和全局网络的权重$ {\rho }^{(0)} $，$ {\theta }^{(0)} $
2: 用全局网络的权重更新每个线程子网络的权重：$ {\rho }^{'(0)}\leftarrow {\rho }^{(0)} $，$ {\theta }^{'(0)}\leftarrow {\theta }^{(0)} $
3: $ {k}_{start}=k $
4: 观察用户当前状态$ {\boldsymbol{s}}^{(k)} $
5: 根据式(21)计算策略分布$ \pi ({\boldsymbol{s}}^{(k)},{\boldsymbol{a}}^{(k)}) $
6: 根据$ \pi ({\boldsymbol{s}}^{(k)},{\boldsymbol{a}}^{(k)}) $选择隐私保护卸载策略$ {\boldsymbol{a}}^{(k)} $
7: 用户完成本地任务处理并将部分任务卸载至多个 MEC 服务器计算，同时开展隐私保护与卸载性能评估
8: 当$ k-{k}_{start}=t $时，执行完$ t $个步骤后开始更新网络参数
(1)$ i=k-1,\cdots,{k}_{start} $时：根据式(24)和(25)分别计算Actor网络和Critic网络的参数
(2)异步更新全局网络的参数$ {\rho }^{(k)} $和$ {\theta }^{(k)} $
(3)更新每个线程子网络的参数：$ {\rho }^{'(k)}\leftarrow {\rho }^{(k)} $，$ {\theta }^{'(k)}\leftarrow {\theta }^{(k)} $
9: 判断算法收敛性，若算法未收敛，令$ k=k+1 $，并转移到步骤3，开始下一时隙的学习

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表 1 AIPCO方案的参数设置

参数名	参数值	参数名	参数值
本地设备CPU频率$ {f}_{\mathrm{local}} $	1 GHz^[7]	MEC服务器的CPU频率$ {f}_{n} $	10 GHz^[11]
计算每比特数据所需周期$ \phi $	1000^[14]	隐私系数$ w $	0.3^[6]
每个CPU周期消耗的能量$ \kappa $	$ {10}^{-27} $ J/cycle^[7]	通信带宽$ B $	0.2 MHz^[16]
用户智能设备总功率$ {p}_{\max } $	1.5 W^[9]	信道噪声功率$ {\delta }^{2} $	$ {10}^{-7} $^[9]

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