Blockchain-Based Reputation Sharing for High-Quality Participant Selection of MCS

Abstract

Mobile crowdsensing (MCS), as a novel large-scale data acquisition method, has attracted more and more attention. Since the participants’ quality directly affects the quality of perceptual task completion in MCS, participant selection has become a focus of researchers. However, due to the sparsity of participants’ information and data privacy, existing solutions have certain limitations in terms of security and accuracy in participant selection. To tackle these problems, this paper proposes a secure and accurate participant selection (SAPS) method. It employs blockchain-based cross-domain reputation sharing while labeling participants with personalized reputation tags as quality references to achieve security and accuracy in participant selection. In particular, SAPS utilizes a model of differential privacy to protect privacy during cross-domain sharing while guaranteeing the credibility of the data sources by leveraging the traceability and non-tamper nature of the blockchain. Comprehensive experiments on real datasets indicate that compared with CMABA, the tasks’ completion quality in SAPS is improved by 18%, and the execution cost of SAPS is reduced by 6%.