BPQA: a Blockchain-based Privacy-Preserving and Quality-Aware Incentive for Mobile Crowdsensing

Mobile Crowdsensing (MCS) struggles to balance data quality with privacy, as existing methods often fail to protect reputation privacy or limit its utility in incentive mechanisms. To bridge this gap, we propose BPQA, a Blockchain-based Privacy preserving and Quality-Aware incentive framework for MCS. BPQA integrates reputation management with cryptographic primitives to enhance data reliability while safeguarding user privacy. Its key innovations include: (1) a reverse-auction-based incentive mechanism that jointly optimizes spatial coverage and worker reliability under budget constraints; (2) a privacy preserving reputation scheme using Pedersen commitments, which supports verifiable updates via additive homomorphism without revealing actual scores; and (3) a triple-layered quality assurance strategy that combines reputation-driven recruitment, range-based data evaluation, and a lightweight secret-sharing truth discovery protocol. A prototype demonstrates BPQA's efficiency, with execution times under 10 seconds for 1,000 worker scenarios and outperforming state-of-the-art schemes (e.g., SPIM-DQA, Trust Worker and PACE) in terms of data quality, reputation availability, and/or computational efficiency. Theoretical analyses prove BPQA's resistance to attacks (e.g., false reporting, inference attacks), while empirical results validate its scalability and practicality for real-world deployments. This work advances the design of secure, privacy-aware MCS, offering a robust solution for applications ranging from environmental monitoring to smart transportation.