Paper Info
Title
Robust Truth Discovery against Data Poisoning in Mobile Crowdsensing
Abstract
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.
Year
DOI
Venue
2019
10.1109/GLOBECOM38437.2019.9013890
2019 IEEE Global Communications Conference (GLOBECOM)
Keywords
DocType
ISSN
data aggregation,source filtering,source estimation,truth discovery algorithms,data poisoning attacks,source reliability,multiple crowdsensing sources,dedicated sensing hardware,crowdsensed information,low-cost commodity sensors,sensing process,mobile crowdsensing paradigm,massive data collection,social settings,advanced sensors,mobile devices,robust truth discovery
Conference
1930-529X
ISBN
Citations 
PageRank 
978-1-7281-0963-3
2
0.39
References 
Authors
0
3
Name
Order
Citations
PageRank
Zonghao Huang120.39
Miao Pan25716.43
Yanmin Gong313316.82