计算机科学
拥挤感测
计算机安全
信息隐私
众包
数据共享
移动设备
数据质量
移动计算
互联网隐私
计算机网络
万维网
病理
经济
公制(单位)
医学
替代医学
运营管理
作者
Cong Zhao,Shusen Yang,Julie A. McCann
标识
DOI:10.1109/tmc.2019.2943468
摘要
The proliferation of mobile smart devices with ever improving sensing capacities means that human-centric Mobile Crowdsensing Systems (MCSs) can economically provide a large scale and flexible sensing solution. The use of personal mobile devices is a sensitive issue, therefore it is mandatory for practical MCSs to preserve private information (the user's true identity, precise location, etc.) while collecting the required sensing data. However, well intentioned privacy protection techniques also conceal autonomous, or even malicious, behaviors of device owners (termed as self-interested), where the objectivity and accuracy of crowdsensing data can therefore be severely threatened. The issue of data quality due to untruthful reporting in privacy-preserving MCSs has been yet to produce solutions. Bringing together game theory, algorithmic mechanism design, and truth discovery, we develop a mechanism to guarantee and enhance the quality of crowdsensing data without jeopardizing the privacy of MCS participants. Together with solid theoretical justifications, we evaluate the performance of our proposal with extensive real-world MCS trace-driven simulations. Experimental results demonstrate the effectiveness of our mechanism on both enhancing the quality of the crowdsensing data and eliminating the motivation of MCS participants, even when their privacy is well protected, to report untruthfully.
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