Securing Task Allocation in Mobile Crowd Sensing: An Incentive Design Approach
Securing Task Allocation in Mobile Crowd Sensing: An Incentive Design Approach
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DOI：
10.1109/cns.2019.8802697
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发表时间：
2019-06
期刊：
影响因子：
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通讯作者：
Mingyan Xiao;Ming Li;Linke Guo;M. Pan;Zhu Han;Pan Li
中科院分区：
文献类型：
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作者：
Mingyan Xiao;Ming Li;Linke Guo;M. Pan;Zhu Han;Pan Li
作为移动人群感知(MCS)的重要组成部分，任务分配问题得到了广泛的研究。总体而言，它解决了如何在传感工作人员之间明智地分配传感任务。然而，其中涉及的安全威胁几乎没有被研究过。在理想的情况下，工作人员被信任向平台报告他们准确的参数，从而可以正确地制定和计算任务分配优化问题。尽管如此，恶意员工可以通过简单地上传伪造的参数来探索非法利益获取。更糟糕的是，这样的攻击很难被发现。在本文中，我们从一个简化的案例开始，在这个案例中，工人们为了获得额外的效用而报告了错误的目标函数。为了防御这种攻击，我们新颖地利用激励机制设计。员工会主动上报理想的“指标”，即使没有员工的真实参数，平台仍能根据这些指标获得准确的任务分配概况。通过形式化分析和大量的仿真结果验证了该机制的有效性和高效性。
As a critical component of mobile crowd sensing (MCS), task allocation has been extensively investigated. In general, it addresses how to wisely distribute sensing tasks among sensing workers. Yet, the security threat involved therein has hardly been studied. In an ideal scenario, workers are trusted to report their accurate parameters to the platform, so that task allocation optimization problems can be correctly formulated and calculated. Nonetheless, malicious workers can explore illegal benefit gain by simply uploading falsified parameters. Even worse, such an attack is difficult to detect. In this paper, we start from a simplified case in which workers report erroneous objective functions to gain extra utility. To defend this attack, we novelly leverage incentive mechanism design. Workers are motivated to report desirable “indicators”, based on which the platform can still obtain the accurate task allocation profile even without workers' genuine parameters. The effectiveness and efficiency of our mechanism is validated through both formal analysis and extensive simulation results.