Incentivizing Crowdsensing-Based Noise Monitoring with Differentially-Private Locations
Incentivizing Crowdsensing-Based Noise Monitoring with Differentially-Private Locations
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DOI：
10.1109/tmc.2019.2946800
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发表时间：
2021-02
影响因子：
7.9
通讯作者：
Pei Huang;Xiaonan Zhang;Linke Guo;Ming Li
中科院分区：
文献类型：
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作者：
Pei Huang;Xiaonan Zhang;Linke Guo;Ming Li
移动的人群感测是其中人群感测服务器将感测任务外包给人群以用于移动的数据收集的技术。在移动的人群感知中，一些任务需要位置信息来实现其目标，例如道路监控、室内平面图重建和智能交通。这一要求的信息引起了对位置隐私泄露的严重关注，并威胁到工人的财产和公共安全。在某些情况下，甚至传感数据本身也可以用作辅助信息，从而导致位置隐私泄露。许多现有的工作应用差分隐私机制的位置隐私保护来解决这个问题，但他们不能有效地实现隐私目标，因为每个工人只考虑他自己的隐私。因此，累积的隐私预算将降低所有工作者位置的组合隐私级别。此外，部署差分隐私对工作人员来说成本高昂，并且会降低人群感知任务所需的数据质量。如何在实现隐私目标的同时平衡成本和提供准确的聚合数据成为一个具有挑战性的问题。在本文中，我们提出了一个组差分私人博弈理论的解决方案，解决了这些限制的隐私保护和有效的方式。我们的计划，使工人的位置和传感数据的不可否认性，而无需一个可信的实体的帮助，同时满足人群传感任务的准确性要求。我们的计划的有效性和效率进行了彻底评估的基础上真实世界的数据集。
Mobile crowd sensing is a technique where a crowd sensing server outsources sensing tasks to the crowd for mobile data collection. In mobile crowd sensing, some tasks require location information to achieve their objectives, such as road monitoring, indoor floor plan reconstruction, and smart transportation. This required information incurs severe concerns on location privacy leakage and threatens workers’ properties as well as public safety. In some cases, even sensing data itself can be used as auxiliary information resulting in location privacy breaches. Many existing works apply differential privacy mechanisms for location privacy preservation to tackle this problem, but they cannot efficiently fulfill privacy goals because each worker only considers his own privacy. As a consequence, the accumulated privacy budget will lower down the composed privacy level of all the workers’ locations. In addition, deploying differential privacy is costly for workers and it will degrade the quality of data required in crowd sensing tasks. How to balance the cost and provide accurate aggregated data while fulfilling privacy objectives becomes a challenging issue. In this paper, we propose a group-differentially-private game-theoretical solution, which addresses these limitations in a privacy-preserving and efficient way. Our scheme enables the indistinguishability of workers’ locations and sensing data without the help of a trusted entity while meeting the accuracy demands of crowd sensing tasks. The effectiveness and efficiency of our scheme are thoroughly evaluated based on real-world datasets.