Location Privacy-Aware Task Offloading in Mobile Edge Computing

Wang, Zhibo; Sun, Yunan; Liu, Defang; Hu, Jiahui; Pang, Xiaoyi; Hu, Yuke; Ren, Kui

doi:10.1109/tmc.2023.3254553

Cited by 15 publications

(10 citation statements)

References 53 publications

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Section: Related Workmentioning

confidence: 99%

“…In recent years, many works have utilized differential-privacy-based location perturbation techniques to effectively protect users' location privacy [14,18]. Differential privacy technology has superior privacy protection effects and can prevent attackers from re-identifying data based on known background knowledge [18,25]. In [18], Wang et al propose a location-privacy-aware task offloading framework (LPA-Offload) that protects user location privacy by using the location perturbation mechanism based on differential privacy.…”

Section: Related Workmentioning

confidence: 99%

“…Differential privacy technology has superior privacy protection effects and can prevent attackers from re-identifying data based on known background knowledge [18,25]. In [18], Wang et al propose a location-privacy-aware task offloading framework (LPA-Offload) that protects user location privacy by using the location perturbation mechanism based on differential privacy. The scheme formulates the optimal offloading strategy based on an iterative method and then calculates the computation cost and privacy leakage.…”

Section: Related Workmentioning

confidence: 99%

“…We assume that the MEC system consists of a MEC server and a user with mobile devices, similar to the work in [17,18]. The user's offloading strategy takes into account the distance between the user and the MEC server, as well as the wireless channel conditions.…”

Section: System Modelmentioning

confidence: 99%

“…In the context of MEC systems, location perturbation is crucial in preserving user privacy by adding noise or modifying sensitive location information. Differential-privacy-based location perturbation techniques have been studied to protect users' location privacy [18]. Thus, this paper investigates location privacy protection in MEC systems based on differential privacy.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Safe-Learning-Based Location-Privacy-Preserved Task Offloading in Mobile Edge Computing

Min,

Liu,

Duan

et al. 2023

Electronics

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Mobile edge computing (MEC) integration with 5G/6G technologies is an essential direction in mobile communications and computing. However, it is crucial to be aware of the potential privacy implications of task offloading in MEC scenarios, specifically the leakage of user location information. To address this issue, this paper proposes a location-privacy-preserved task offloading (LPTO) scheme based on safe reinforcement learning to balance computational cost and privacy protection. This scheme uses the differential privacy technique to perturb the user’s actual location to achieve location privacy protection. We model the privacy-preserving location perturbation problem as a Markov decision process (MDP), and we develop a safe deep Q-network (DQN)-based LPTO (SDLPTO) scheme to select the offloading policy and location perturbation policy dynamically. This approach effectively mitigates the selection of high-risk state–action pairs by conducting a risk assessment for each state–action pair. Simulation results show that the proposed SDLPTO scheme has a lower computational cost and location privacy leakage than the benchmarks. These results highlight the significance of our approach in protecting user location privacy while achieving improved performance in MEC environments.

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