$D^{2}MTS$: Enabling Dependable Data Collection With Multiple Crowdsourcers Trust Sharing in Mobile Crowdsensing

When enjoying mobile crowdsensing (MCS), it is vital to evaluate the trustworthiness of mobile users (MUs) without disclosing their sensitive information. However, the existing schemes ignore this requirement in the multiple crowdsourcers (CSs) scenario. The lack of a credible sharing about MUs’ trustworthiness results in an inaccurate trust evaluation, disabling allocating tasks to reliable MUs. To address it, based on the analysis of the desired properties, we propose a scheme enabling dependable data collection with multiple crowdsourcers trust sharing ($D^{2}MTS$D2MTS). Specifically, we design the MU anonymous management. Two kinds of MU generated pseudonym systems without relationships are presented to mark each MU in trust evaluation and task execution, respectively. Through the devised pseudonym changes on these pseudonyms and the common token distribution algorithm, $D^{2}MTS$D2MTS realizes privacy-preserving trust sharing. Moreover, to guarantee credible sharing, based on the hash chain, $D^{2}MTS$D2MTS records MUs’ trustworthiness with the unforgeable signature on the blockchain established by multiple CSs which do not trust each other naturally. Extensive experiments show that compared with the other works, $D^{2}MTS$D2MTS's detection ratio of vicious MUs and the percentage of reliable MUs among the selected ones can increase by 208.61% and 28.27%. Both computational and communication delays are limited.