Lightweight privacy-preserving truth discovery for vehicular air quality monitoring

Date

2023

Authors

Liu, Rui
Pan, Jianping

Journal Title

Journal ISSN

Volume Title

Publisher

Digital Communications and Networks

Abstract

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.

Description

Keywords

Privacy preserving, Truth discovery, Crowdsensing, Vehicular networks

Citation

Liu, R., & Pan, J. (2023). Lightweight privacy-preserving truth discovery for vehicular air quality monitoring. Digital Communications and Networks, 9(1), 280-291. https://doi.org/10.1016/j.dcan.2022.03.021.