Lightweight privacy-preserving truth discovery for vehicular air quality monitoring

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