Bilateral Task-Driven Privacy-Preserving Data Acquisition for Crowdsensed Data Trading

Crowdsensed data trading (CDT) solves the problem of data resource scarcity and diversity, faced in conventional data trading by dispatching workers to perform data collection tasks and sharing data through trading. In CDT, both worker and data requesters need to provide geographic location or task location information for spatiotemporal data collection tasks. Existing research has insufficiently addressed the simultaneous consideration of both location privacy information and overlooked the variability in data quality resulting from variations in worker task accessibility and location. To address this problem, we propose a privacy-preserving task allocation scheme with regional coverage based on homomorphic encryption, which allows workers to perform tasks within the qualified region, the degree of regional coverage is associated with data quality to provide diversified data. To solve the sensing data trading and allocation problem for many-to-many users, we further introduce double auction. And thus propose a privacy-preserving data trading scheme to protect bidding information privacy, this scheme ensures the truthfulness of the auction process and mitigates participant manipulation. Besides, we employ a secure multiparty computing strategy to implement truth discovery in CDT, which enables third-party platforms to perform accurate task allocation and winner decisions based on encrypted location and bidding information. Extensive theoretical and simulation analyses show that the proposed scheme satisfies the expected economic properties (truthfulness, individual rationality, etc.), privacy, and effectiveness.