Managing Scores of Crowdsourcing Workers Using Blockchain

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.

I. Introduction

In recent years, crowdsourcing, in which service providers provide various services by outsourcing them to an unspecified number of workers, has attracted wide attention [8]. Crowd-sourcing consists of a client, a worker, and a service provider. A worker selects and executes a task that can be executed by his or herself and submits the results to a crowdsourcing system after completing the task. If the provided results are matched with those requested by the client, the worker can receive a reward as compensation for the task. Widely known examples of services using crowdsourcing include Amazon Mechanical Turk, Uber Taxi, Uber Eats, and Lancers.