BFCrowd: Federated Crowdsourcing With Privacy-Aware and Fine-Grained Task Matching Via Blockchain

Nowadays, crowdsourcing has evolved into a cost-efficient and scalable task execution paradigm that benefits both task requesters and workers. Task matching is a crucial crowdsourcing procedure for deciding the task execution quality, but security and privacy concerns arise as the crowdsourcing platform cannot be fully trusted. Existing privacy-aware task-matching schemes are limited to intra-platform central matching in the semi-honest model and coarse-grained keyword/location-based matching over one single attribute. Solutions supporting secure cross-platform and fine-grained task matching in the malicious model are urgently needed. In this paper, we first formally defined BFCrowd, a federated crowdsourcing system built on a consortium blockchain. BFCrowd aggregates multi-platform resources and enables decentralized and reliable cross-platform task matching using smart contracts, in the presence of malicious workers and platforms. Notably, we design a fully secure ciphertext-policy attribute-based encryption scheme with concealed access policies and user-side lightweight decryption, which thoroughly caters to the dual-side privacy demand and resource-limited workers and serves for fine-grained expressive task matching over multiple attributes. Moreover, it supports comparison over numerical attributes. Formal security analysis proves the desirable privacy guarantees in the standard model and collusion resistance. Extensive experiments implemented atop Hyperledger Fabric demonstrate both on-chain and off-chain performance.