Privacy-preserving worker selection in spatial crowdsourcing based on quantum homomorphic encryption

In spatial crowdsourcing tasks, the risk of privacy leakage regarding task and workers’ locations has always been a critical issue. Although many scholars have proposed solutions based on classical computing, these methods cannot withstand potential future quantum computing attacks. This paper presents a quantum-secure protocol for selecting workers in spatial crowdsourcing, which utilizes a two-stage filtering process combining quantum multi-party geometric intersection and quantum homomorphic encryption. The protocol ensures that workers are selected without disclosing the privacy information of any participants. Compared to traditional classical protocols, our solution offers significantly higher security. The correctness analysis validates the feasibility and effectiveness of the protocol.