边缘计算下面向位置隐私保护的中继分流模型

随着基于位置服务(location-based services, LBS)的广泛使用,人们越来越关注位置隐私的保护问题.基于假人的位置隐私保护方法通过在真实位置信息中混入多个虚假位置信息,能够有效保护用户位置隐私.然而,传统基于假人的位置隐私保护方案大多部署在云端的远程服务器中,其面临着用户获取结果时延过高的性能瓶颈.针对上述问题,本文将基于假人的位置隐私保护方法迁移到边缘计算环境下进行部署,并针对边缘服务器的服务能力与覆盖范围具有上限的特点,提出了面向位置隐私保护的中继分流模型,实现了其中的分流方法,并在真实数据集中运行了本文所提出的方法.实验结果表明该方法在保证用户位置隐私保护效果的同时,可以降低用户获取结果的时延.

Relay-Assisted Offloading Model for Location Privacy Protection under Edge Computing

With the widespread adoption of location-based services, people pay more and more attention to the issue of location privacy protection. The dummy based location privacy protection method could achieve the goal via mixing fake locations with users; real location. However, most of traditional dummy based location privacy protection methods are deployed in remote cloud servers, which brings performance limitation issue such as long delay for users to obtain computation results. To address this problem, we consider migrating the dummy based location privacy protection method from cloud servers to edge servers. Moreover, in view of the upper limit of service capacity and coverage of edge servers, we propose a relay offloading model for location privacy protection. We implement the offloading method and run our method with a real data set. Experimental results verify that our method can reduce the delay for user to obtain computation results while ensuring the effect of user;s location privacy protection.