PrivCrowd: A Secure Blockchain-Based Crowdsourcing Framework with Fine-Grained Worker Selection

Abstract

Blockchain-based crowdsourcing systems can mitigate some known limitations of the centralized crowdsourcing platform, such as single point of failure and Sybil attacks. However, blockchain-based crowdsourcing systems still endure the issues of privacy and security. Participants' sensitive information (e.g., identity, address, and expertise) have the risk of privacy disclosure. Sensitive crowdsourcing tasks such as location-based data collection and labeling images including faces also need privacy-preserving. Moreover, current work fails to balance the anonymity and public auditing of workers. In this paper, we present a secure blockchain-based crowdsourcing framework with fine-grained worker selection, named PrivCrowd which exploits a functional encryption scheme to protect the data privacy of tasks and to select workers by matching the attributes. In PrivCrowd, requesters and workers can achieve both exchange and evaluation fairness by calling smart contracts. Solutions collection also can be done in a secure, sound, and noninteractive way. Experiment results show the feasibility, usability, and efficiency of PrivCrowd.