Achieving lightweight, efficient, privacy-preserving user recruitment in mobile crowdsensing

Abstract

The emergence of mobile crowdsensing (MCS) has revolutionized data collection method. As an important means of guaranteeing data quality, user recruitment is critical to sensing task completion. Aiming at the problem of user privacy disclosure in user recruitment, particularly when sensing platforms lack prior knowledge of user quality, we propose a Privacy-Preserving User Recruitment scheme (PPUR) which can maximize sensing quality in a lightweight and efficient manner. We design multiple secure protocols for both user quality calculation and user recruitment based on additive secret sharing (ASS). Specifically, we propose Secure user Quality Calculation (SQC) protocol to assess user quality instead of requiring user interaction in the case of unknown ground truth. Combinatorial multi-armed bandit (CMAB) based Secure User Recruitment (SUR) protocol, effectively tackles the challenge of recruiting multiple users without prior knowledge and user interactivity while adhering to budget and time limitations. Theoretical analysis confirms lightweight overhead of the PPUR scheme and its multi-class data security. Experimental results show that SQC has superior performance in both computational cost and communication overhead. The regret indicator’s findings demonstrate that SUR can effectively utilize budget and time to achieve optimal user recruitment decision.