Authors: Qi Feng, Debiao He, Min Luo, Xinyi Huang, Kim-Kwang Raymond Choo

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

Journal: IEEE Transactions on Computers

Abstract: 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 Efficient and Privacy-preserving Real-time Incentive system for CrowdsEnsing (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 three orders of magnitude compared with other competing schemes.