Blockchain-based Privacy-Preserving Reputation Management for Crowdsensing

Abstract

The performance of mobile crowdsensing systems heavily depends on individuals’ participation and the quality of sensing data, because of which the reputation mechanism is of great necessity. However, the mechanism cannot function well if the workers in a crowdsensing system do not behave properly. In this paper, we propose a blockchain-based crowdsensing framework that can resist against the threats to data quality without compromising workers’ privacy. In the proposed framework, a worker is allowed to use different pseudonyms to protect privacy. While the use of pseudonyms will obstruct service providers from evaluating the worker’s reputation. To deal with this issue, we treat reputation as a special type of token, and design a ring signature-based method to anonymously transfer a worker’s reputation score from one pseudonym to another. Moreover, to prevent a malicious worker from taking the advantage of pseudonyms to alter his reputation score, we propose two authentication methods that can verify the legitimacy of a worker’s pseudonym in a privacy-preserving way. The effectiveness of the proposed methods is analyzed theoretically and experimentally.