Network Location Privacy Protection with Multi-access Edge Computing

Zhang, Ping; Durrësi, Mimoza; Durresi, Arjan

doi:10.1007/978-3-030-15032-7_112

Cited by 3 publications

(3 citation statements)

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“…As a result, the edge server only knows the location of the proxy server but does not get the real location of the mobile device. According to [ 30 ], when the mobile device u offloads task i to edge server j via proxy server k , its location privacy denoted by

can be expressed as

where

is the proxy server k and

is the edge server j .

is the distance between two devices a and b , which is calculated as follows:

…”

Section: Models and Problem Definitionmentioning

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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can be expressed as

where

is the proxy server k and

is the edge server j .

is the distance between two devices a and b , which is calculated as follows:

…”

Section: Models and Problem Definitionmentioning

confidence: 99%

“…

is the position of device b in the x-axis direction and

is the position of device b in the y-axis direction. However, in [ 30 ], no specific task offloading scenario is considered. In fact, not all tasks need to be offloaded to edge servers for execution.…”

Section: Models and Problem Definitionmentioning

confidence: 99%

Privacy-Preserving Task Offloading Strategies in MEC

Liu

et al. 2022

Sensors

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“…Several works considered differentially private data locations services for edge computing [41][42][43][44]. The papers use different definitions of differential privacy to protect the exact location of the individual vehicle.…”

Section: Related Workmentioning

confidence: 99%

Wavelet-based dynamic and privacy-preserving similitude data models for edge computing

Derbeko

Gudes

2020

Wireless Netw

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The privacy-preserving data release is an increasingly important problem in today's computing. As the end devices collect more and more data, reducing the amount of published data saves considerable network, CPU and storage resources. The savings are especially important for constrained end devices that collect and send large amounts of data, especially over wireless networks. We propose the use of query-independent, similitude models for privacy-preserving data release on the end devices. The conducted experiments validate that the wavelet-based similitude model maintains an accuracy compared to other state-of-the-art methods while compressing the model. Expanding on our previous work (Derbeko et al. in: Cyber security cryptography and machine learning-second international symposium, CSCML 2018, Beer Sheva, Israel, 2018) we show how wavelet-based similitude models can be combined and ''subtracted'' when new end devices appear or leave the system. Experiments show that accuracy is the same or improved with a model composition. This data-oriented approach allows further processing near the end devices in a fog or a similar edge computing concept.

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