Lightweight privacy-preserving truth discovery for vehicular air quality monitoring  

作　　者：Rui Liu Jianping Pan 

机构地区：[1]Department of Computer Science,University of Victoria,Victoria,BC,V8P 5C2,Canada

出　　处：《Digital Communications and Networks》2023年第1期280-291,共12页数字通信与网络（英文版）

摘　　要：Air pollution has become a global concern for many years.Vehicular crowdsensing systems make it possible to monitor air quality at a fine granularity.To better utilize the sensory data with varying credibility,truth discovery frameworks are introduced.However,in urban cities,there is a significant difference in traffic volumes of streets or blocks,which leads to a data sparsity problem for truth discovery.Protecting the privacy of participant vehicles is also a crucial task.We first present a data masking-based privacy-preserving truth discovery framework,which incorporates spatial and temporal correlations to solve the sparsity problem.To further improve the truth discovery performance of the presented framework,an enhanced version is proposed with anonymous communication and data perturbation.Both frameworks are more lightweight than the existing cryptography-based methods.We also evaluate the work with simulations and fully discuss the performance and possible extensions.

关 键 词：Privacy preserving Truth discovery Crowdsensing Vehicular networks 

分 类 号：X51[环境科学与工程—环境工程]