Matching as You Want: A Decentralized, Flexible, and Efficient Realization for Crowdsourcing with Dual-Side Privacy

As the first service procedure in crowdsourcing, task matching is crucial for users and has aroused extensive attention. However, due to the submission of sensitive information, task requesters and workers have growing concerns about matching security and privacy, as well as efficiency and flexibility for service quality. Prior privacy-aware task-matching resolutions either rely on a central semi-honest crowdsourcing platform for matching integrity, or still suffer from low efficiency, limited privacy considerations, and inflexibility even if blockchain is incorporated for decentralized matching. In this paper, we construct a decentralized, secure, and flexibly expressive crowdsourcing task-matching system robust to misbehaviors based on consortium blockchain. Particularly, to support fine-grained worker selection and worker-side task search with dual-side privacy under no central trust, we propose a multi-authority policy-hiding attribute-based encryption scheme with keyword search, enforced by smart contracts. We optimize the ciphertext and key size by designing a novel approach for policy and attribute vector generation, meanwhile immune to malicious workers submitting incorrect vectors. Such a verifiable vector generation approach exploits verifiable multiplicative homomorphic secret sharing and Viète's formulas. Formal security analysis and extensive experiments conducted over Hyperledger Fabric demonstrate the desired security properties and superior on-chain and off-chain performance.