Robust Truth Discovery against Data Poisoning in Mobile Crowdsensing
Robust Truth Discovery against Data Poisoning in Mobile Crowdsensing
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DOI：
10.1109/globecom38437.2019.9013890
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发表时间：
2019-12
期刊：
影响因子：
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通讯作者：
Zonghao Huang;M. Pan;Yanmin Gong
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文献类型：
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作者：
Zonghao Huang;M. Pan;Yanmin Gong
如今，大多数移动的设备都配备了先进的传感器，能够测量有关周围环境或社交设置的信息。移动的设备的普遍存在使其成为海量数据收集的完美平台，这激发了移动的人群感知范式的出现。然而，由于感测过程的固有噪声性质和低成本商品传感器的有限能力，与通过专用感测硬件的感测结果相比，群体感测信息往往不太可靠，并且多个群体感测源可能彼此冲突。因此，重要的是要解决收集的数据中的冲突，并发现潜在的真相。传统的真值发现方法通常对数据源的可靠性进行估计，并根据源的可靠性来预测真值。然而，最近的数据中毒攻击大大降低了现有的真理发现算法的性能，攻击者的目标是最大限度地提高效用损失。在本文中，我们调查的数据中毒攻击的真相发现，并提出了一个强大的方法来对付这种攻击，通过额外的源估计和源过滤数据聚合之前。基于真实世界的数据，我们模拟我们的方法，并评估其性能下的数据中毒攻击，证明了我们的方法的鲁棒性。
Nowadays most mobile devices are equipped with advanced sensors, enabling the measurement of information about surrounding environment or social settings. The ubiquity of mobile devices makes them the perfect platform for massive data collection, which motivates the emergence of mobile crowdsensing paradigm. However, due to the inherent noisy nature of the sensing process and the limited capability of low-cost commodity sensors, crowdsensed information tends to be less reliable compared with sensing results through dedicated sensing hardware, and multiple crowdsensing sources may conflict with each other. Thus, it is important to resolve conflicts in the collected data and discover the underlying truth. Traditional truth discovery approaches usually estimate the reliability of data sources and predict the truth value based on source reliability. However, recent data poisoning attacks greatly degrade the performance of existing truth discovery algorithms, where attackers aim to maximize the utility loss. In this paper, we investigate the data poisoning attacks on truth discovery and propose a robust approach against such attacks through additional source estimation and source filtering before data aggregation. Based on real-world data, we simulate our approach and evaluate its performance under data poisoning attacks, demonstrating the robustness of our approach.