Privacy-Preserving Reputation Management for Blockchain-Based Mobile Crowdsensing

Abstract

Mobile crowdsensing (MCS) is an emerging data acquisition technique that combines crowdsourcing with mobile devices to collect massive data in a cost-satisfactory manner. Two notable challenges of MCS are leakage of privacy and the challenge of malicious users, privacy-preserving reputation management scheme is an efficient method to tackle these challenges. However, most existing schemes rely on a semi-honest server and process data in plaintext domain without considering single point of failure and privacy of participants. In this paper we propose a reputation management scheme with blockchain to identify malicious users and protect users’ privacy simultaneously in MCS scenario. The secure and open nature of blockchain are exploited to build a dependable and efficient reputation management platform. Moreover, we adopt a distributed computing algorithm, Eigentrust, to construct a distributed reputation management framework, nevertheless, it neglects to preserve users’ privacy. So we leverage a verifiable secret sharing scheme into Eigentrust algorithm, which can prevent users’ personal information from being disclosed. The extensive analysis and experiments performed on EOS blockchain demonstrate that our system can effectively identify malicious users while preserving privacy.