EPRICE: An Efficient and Privacy-Preserving Real-Time Incentive System for Crowdsensing in Industrial Internet of Things

In crowdsensing, we can leverage intelligent devices and real-time incentive mechanisms to facilitate the collection of reliable and timely data in Industrial Internet of Things (IIoT) settings. In such a setting, one can use cryptographic primitives to support data privacy preservation and quality-aware reward distribution simultaneously. However, existing approaches might incur expensive computation costs, suffer from overflow problems, or rely on implicit security conditions. In this paper, we propose an <underline>E</underline>fficient and <underline>P</underline>rivacy-preserving <underline>R</underline>eal-time <underline>I</underline>ncentive system for <underline>C</underline>rowds<underline>E</underline>nsing (EPRICE), designed to estimate the reliability of sensing data in a privacy-preserving setting. The theoretical analysis demonstrates that our proposed system achieves a high level of privacy-preserving for real-time reward distribution and supports practical privacy-preserving properties. The experimental findings show that our proposed EPRICE system significantly decreases the computation costs by <italic>three orders of magnitude</italic> compared with other competing schemes.