Title: Preserving privacy in mobile crowdsensing

Authors: Bayan Hashr Saeed Alamri; Muhammad Mostafa Monowar; Suhair Alshehri; Mohammad Haseeb Zafar; Iftikhar Ahmad Khan

Addresses: Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia ' Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia ' Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia ' Cardiff School of Technologies, Cardiff Metropolitan University, Cardiff, UK ' Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia

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.

Keywords: mobile crowdsensing; MCS; privacy preservation; data reliability; untrustworthy; incentive; user recruitment.

DOI: 10.1504/IJSNET.2022.127838

International Journal of Sensor Networks, 2022 Vol.40 No.4, pp.217 - 237

Received: 09 Feb 2022
Accepted: 08 Jun 2022
Published online: 19 Dec 2022 *

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