TrustWorker: A Trustworthy and Privacy-Preserving Worker Selection Scheme for Blockchain-Based Crowdsensing

Worker selection in crowdsensing plays an important role in the quality control of sensing services. The majority of existing studies on worker selection were largely dependent on a trusted centralized server, which might suffer from single point of failure, the lack of transparency and so on. Some works recently proposed blockchain-based crowdsensing, which utilized reputation values stored on blockchains to select trusted workers. However, the transparency of blockchains enables attackers to effectively infer private information about workers by the disclosure of their reputation values. In this article, we proposed the TrustWorker, a trustworthy and privacy-preserving worker selection scheme for blockchain-based crowdsensing. By taking the advantages of blockchains such as decentralization, transparency and immutability, our TrustWorker could make the worker selection process trustworthy. To protect workers’ reputation privacy in our TrustWorker, we adopted a deterministic encryption algorithm to encrypt reputation values and then selected the top $N$ workers in the light of secret minimum heapsort scheme. Finally, we theoretically analyzed the effectiveness and efficiency of our TrustWorker, and then conducted a series of experiments. The theoretical analysis and experiment results demonstrate that our TrustWorker can achieve trustworthy worker selection, while ensuring the workers’ privacy and the high quality of sensing services.

1 Introduction

With the pervasiveness of smart devices and wireless communications, crowdsensing as a new sensing paradigm has emerged [1]. As a subclass of crowdsourcing, it empowers individuals with sensor-rich smart devices to conduct large-scale data collection without deploying static sensors [2]. Because of the advantages on extensive coverage, low deployment cost and so forth, it has attracted researchers from various fields such as traffic [3], environment [4], and healthcare [5]. Typically, there are three entities involved, named a requester, a group of workers and a trusted centralized server (TCS) [6]. Generally, the requester delegates operations such as task allocation, worker selection, results aggregation and disputes arbitration to the TCS [7]. There are many representative examples of crowdsensing. For example, Upwork [8] is currently the world's largest freelance market, which requires requesters to deposit a certain amount of payment into their escrow account before posting tasks. Based on this platform, requesters can hire workers to design or write, and workers compete with each other to obtain opportunities for task execution and rewards. In addition, both the winning workers and requesters need to pay a certain percentage of processing fee to Upwork. In Amazon Mechanical Turk∼(MTurk) [9], requesters can be individuals or companies, and they pay to recruit workers. Workers interested in tasks can submit the request for participation on the platform. MTurk labor market has been widely used by researchers in various fields to recruit workers for data collection and data labeling. Essentially, both Upwork and MTurk are a trusted centralized platform. Apparently, the success or not of crowdsensing rests on the traditional trust-based model, which might suffer from the inevitable issues such as single-point failure, privacy leakage, the lack of transparency in operations and performance bottleneck. There have been some works that use the technical advantages of blockchains such as decentralization, transparency and immutability to build blockchain-based crowdsensing (BBC) to alleviate these issues caused by TCS [10], [11], [12], [13]. However, none of them intensively investigate the worker selection problem in BBC.