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.

翻译：空气污染多年来已成为全球关注的问题。车辆群智感知系统使得细粒度空气质量监测成为可能。为更好地利用可信度各异的传感数据，引入了真值发现框架。然而，在城市中，不同街道或街区的交通流量存在显著差异，导致真值发现面临数据稀疏问题。同时，保护参与车辆的隐私也是一项关键任务。我们首先提出一种基于数据掩码的隐私保护真值发现框架，该框架融合空间与时间相关性以解决稀疏性问题。为进一步提升所提出框架的真值发现性能，我们提出了结合匿名通信与数据扰动的增强版本。这两种框架均比现有基于密码学的方法更加轻量级。我们通过仿真实验对工作进行了评估，并充分讨论了其性能与可能的扩展方向。