With the advancement of mobile crowd sensing systems and vehicular ad hoc networks, the human-carried mobile devices (e.g., smartphones, smart navigators, and smart tablets) equipped with a variety of sensors (such as GPS, accelerometer, and compass) can work together to collect sensory data consequently delivered to the cloud for processing purposes, which supports a wide range of promising applications such as traffic monitoring, path planning, and real-time navigation. To ensure the authenticity and privacy of data, privacy-preserving truth discovery has attracted much attention since it can find reliable information among uneven quality of data collected from mobile users, while protecting both the confidentiality of users' raw sensory data and reliability. However, these methods always incur tremendous overhead and require all participants to keep online for interacting frequently with the cloud server. In this paper, we design an efficient and privacy-preserving truth discovery (EPTD) approach in mobile crowd sensing systems, which can tolerate users offline at any stage, while guaranteeing practical efficiency and accuracy under working process. More notably, our EPTD is the first solution to resolve the problem that users must be online all times during the truth discovery under a single cloud server setting. Moreover, we design a double-masking protocol to ensure the strong security of users' privacy even if the cloud server colludes with multiple users. Extensive experiments conducted on real-world mobile crowd sensing systems also demonstrate the high performance of our proposed scheme compared with existing models.