Privacy-Preserving Task Distribution Mechanism with Cloud-Edge IoT for the Mobile Crowdsensing

Abstract

Mobile crowdsensing under big data provides an efficient, win-win, and low-budget data collection solution for IoT applications such as the smart city. However, its open and all access scenarios raise the threat of data security and user privacy during task distribution of mobile crowdsensing. To eliminate the above threat, this paper first designs a privacy-preserving task distribution scheme (Scheme 1), which realizes fine-grained access control and the practical keyword search, as well as protects the access policy. But it incurs expensive computational and communication consumptions for the task performer side. In this regard, we construct Scheme 2 to attain a lightweight trapdoor generation and keyword search mechanism, and it enables the crowdsensing platform to predecrypt a ciphertext without revealing any information about the task and the performer’s privacy. Then, the resource-constrained device on the task performer side can recover the task with a few computational and communication overheads. The security of the scheme has been detailedly proved and analyzed, and theoretical comparisons and experiment demonstrate their practicability.