Privacy-Preserving Bilateral Reputation Evaluation in Blockchain Based Crowdsensing

Reputation management is an effective way to improve the quality of data collection in mobile crowdsensing, but existing subjective evaluation-based schemes are difficult to realize the real evaluation of worker reputation under privacy protection. To this end, we propose a bilateral reputation evaluation privacy-preserving (BREPP) scheme based on blockchain. The scheme firstly adopts random interference and Pedersen Commitment to hide the parameters of the workers’ reputation update, and utilizes the compact linkable spontaneous anonymous group (CLSAG) signature to anonymously verify the reputation update process. Besides, it employs a combination of Bulletproofs and Schnnor signature to verify the reputation feedback process of the workers. Then, the sub-address method is adopted to achieve fast location retrieval when the identities of the workers and requesters are anonymized. Finally, a prototype system is deployed on Hyperledger Fabric to evaluate the performance of the reputation update. The simulation results show that compared with existing reputation privacy protection schemes, BREPP scheme reduces the total reputation management delay and reputation evaluation error rate by at least 2.00% and 24.96%, respectively. Compared with schemes without privacy protection, our scheme achieves a better balance between reputation evaluation accuracy and total delay.