Anonymous Task Assignment and Worker Payment in Mobile Crowdsensing

Abstract

Ensuring efficient task assignment and secure payment in mobile crowdsensing while preserving worker location privacy remains a challenging problem. Existing solutions either rely on expensive encryption schemes, employ blockchain-based verification that incurs high computational and gas costs, or use differential privacy techniques that degrade spatial accuracy. This paper introduces the Privacy-preserving Task Assignment and Payment (PTAP) framework, a lightweight solution built upon secure multi-party computation (SMPC). PTAP employs additive secret sharing and a challenge-response mechanism across three semi-honest servers to achieve anonymous task allocation and payment without blockchain or zero-knowledge proofs. The framework guarantees full unlinkability between worker identities, task locations, and payment records while maintaining accurate location-based assignment and supporting traceability for dispute resolution. Experimental evaluation using the MP-SPDZ framework demonstrates scalability to over 1.5 million workers and 7 million payment tokens. The average end-to-end completion time is approximately 35.4 seconds, with zero gas cost. Compared to the state-of-the-art AVeCQ system [15], which requires about 13 minutes and 37 MWei per transaction on the Goerli network for only 1,024 users. The results confirm PTAP's efficiency, scalability, and strong privacy guarantees for large-scale mobile crowdsensing deployments.