  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.
