Privacy-by-Design Distributed Offloading for Vehicular Edge Computing

Ma, Weibin; Mashayekhy, Lena

doi:10.1145/3344341.3368804

Cited by 3 publications

(3 citation statements)

References 23 publications

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“…In [ 13 , 14 , 15 , 16 , 17 ], privacy conflicts between tasks were mainly studied to reduce the risk of association privacy leakage. In [ 13 ], Peng et al.…”

Section: Related Researchmentioning

confidence: 99%

“…presented an IoT-oriented privacy-preserving offloading method, considering privacy conflicts between tasks. In [ 17 ], Ma and Mashayekhy offered a privacy-designed offloading solution for vehicular edge computing considering privacy conflicts between tasks, to meet the delay requirements and reduce the energy consumption of vehicles.…”

Section: Related Researchmentioning

confidence: 99%

“…In [ 9 , 10 , 11 , 12 ], authors have studied the location privacy problem for task offloading in MEC. In [ 13 , 14 , 15 , 16 , 17 ], authors have investigated how to reduce the risk of association privacy leakage by reducing the privacy conflicts of tasks. In [ 18 , 19 ], based on the concept of information entropy, authors have quantified data privacy to avoid the leakage of data privacy.…”

Section: Introductionmentioning

confidence: 99%

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Privacy-Preserving Task Offloading Strategies in MEC

Liu

et al. 2022

Sensors

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In mobile edge computing (MEC), mobile devices can choose to offload their tasks to edge servers for execution, thereby effectively reducing the completion time of tasks and energy consumption of mobile devices. However, most of the data transfer brought by offloading relies on wireless communication technology, making the private information of mobile devices vulnerable to eavesdropping and monitoring. Privacy leakage, especially the location and association privacies, can pose a significant risk to users of mobile devices. Therefore, protecting the privacy of mobile devices during task offloading is important and cannot be ignored. This paper considers both location privacy and association privacy of mobile devices during task offloading in MEC and targets to reduce the leakage of location and association privacy while minimizing the average completion time of tasks. To achieve these goals, we design a privacy-preserving task offloading scheme to protect location privacy and association privacy. The scheme is mainly divided into two parts. First, we adopt a proxy forwarding mechanism to protect the location privacy of mobile devices from being leaked. Second, we select the proxy server and edge server for each task that needs to be offloaded. In the proxy server selection policy, we make a choice based on the location information of proxy servers, to reduce the leakage risk of location privacy. In the edge server selection strategy, we consider the privacy conflict between tasks, the computing ability, and location of edge servers, to reduce the leakage risk of association privacy plus the average completion time of tasks as much as possible. Simulated experimental results demonstrate that our scheme is effective in protecting the location privacy and association privacy of mobile devices and reducing the average completion time of tasks compared with the-state-of-art techniques.

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“…In [ 13 , 14 , 15 , 16 , 17 ], privacy conflicts between tasks were mainly studied to reduce the risk of association privacy leakage. In [ 13 ], Peng et al.…”

Section: Related Researchmentioning

confidence: 99%

Section: Related Researchmentioning

confidence: 99%