Privacy-Preserving Reputation Management for Edge Computing Enhanced Mobile Crowdsensing
Privacy-Preserving Reputation Management for Edge Computing Enhanced Mobile Crowdsensing
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DOI：
10.1109/tsc.2018.2825986
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发表时间：
2019-09-01
影响因子：
8.1
通讯作者：
Xiang, Yong
中科院分区：
文献类型：
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作者：
Ma, Lichuan;Liu, Xuefeng;Xiang, Yong
移动的人群感知（MCS）因其利用单个移动的设备来感知、收集和分析数据而不是部署传感器的潜力而受到欢迎。随着传感数据变得越来越细粒度和复杂，有一种趋势是用边缘计算范式来增强MCS，以减少时间延迟和高带宽成本。传感数据可能会泄露个人信息，因此保护参与者的隐私具有重要意义。然而，保护隐私可能会阻碍处理恶意参与者的过程。在本文中，我们提出了两个隐私保护信誉管理方案的边缘计算增强MCS同时保护隐私和处理恶意参与者。在基本方案中，设计了一种新的信誉值更新方法，该方法基于加密后的感知数据与最终聚合结果的偏差。基本方案是有效的，但代价是向声誉管理者揭示每个参与者的偏差值。为了克服这个缺点，我们提出了一个先进的计划，通过更新信誉值利用偏差的秩。大量的实验表明，这两种方案都具有较高的成本效率，并能有效地对付恶意参与者。
Mobile crowdsensing (MCS) has gained popularity for its potential to leverage individual mobile devices to sense, collect, and analyze data instead of deploying sensors. As the sensing data become increasingly fine-grained and complicated, there is a tendency to enhance MCS with the edge computing paradigm to reduce time delays and high bandwidth costs. The sensing data may reveal personal information, and thus it is of great significance to preserve the privacy of the participants. However, preserving privacy may hinder the process of handling malicious participants. In this paper, we propose two privacy preserving reputation management schemes for edge computing enhanced MCS to simultaneously preserve privacy and deal with malicious participants. In the basic scheme, a novel reputation value updating method is designed based on the deviations of the encrypted sensing data from the final aggregating result. The basic scheme is efficient at the expense of revealing the deviation value of each participant to the reputation manager. To conquer this drawback, we propose an advanced scheme by updating the reputation values utilizing the rank of deviations. Extensive experiments demonstrate that both these two schemes have high cost efficiency and are effective to deal with malicious participants.