Abstract
Mobile crowdsensing (MCS) is a technique where individuals voluntarily utilise their devices to collect data to measure phenomena. In this article, a review of privacy-preserving in MCS is presented. First, it highlights MCS definitions, architecture, and unique characteristics. Then, it provides background knowledge about MCS. Afterward, a privacy-oriented MCS taxonomy in terms of privacy-oriented; data reliability, incentive, and task allocation user recruitment mechanisms, is devised. This work explores contemporary state-of-the-art issues related to privacy and security. It reviews 35 recent research published by high-quality sources and provides a topic-oriented survey for these efforts. It shows that only 16% of the papers evaluate their schemes through experiments on real smartphones, and Huawei is the most widely used mobile (45%). It shows an increasing trend in publications from 2017 till now. It highlights recent challenges faced the privacy in MCS and potential research directions for developing more advanced methods to optimise MCS.