Managing Scores of Crowdsourcing Workers Using Blockchain
Kenta Konomi, Noriaki Kamiyama · 2022 IEEE 33rd Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC) · 2022
In recent years, crowdsourcing, in which various workers provide computing and human resources for requested jobs through networks, has attracted wide attention as an efficient matching system between service users and workers. In crowdsourcing, a service provider needs to evaluate each worker on the basis of their contribution in order to fairly reward workers, so the provider is required to manage the private data of workers, e.g., trajectories and behaviors of workers. However, in crowdsourcing, a single service provider manages all the private data of workers, so there is a possibility that this data might be leaked or manipulated when the provider is attacked by malicious users. Moreover, employees of a service provider might abuse the private data of workers. To solve these problems, we propose managing the private data of workers by using the blockchain for crowdsourcing. In particular, we focus on a type of crowdsourcing in which workers have a direct contract with the service provider providing a service to users, e.g., Uber Eats. Moreover, we also propose a method for evaluating worker scores that uses an entropy weight to weight each evaluation criterion of workers on the basis of the distribution of each criterion. Through a computer simulation using data of cycling conditions obtained by the motion sensor of smartphones attached to bicycles, we evaluate the effectiveness of the proposed system when applied to worker evaluation for a food delivery service, e.g., Uber Eats.