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
E
fficient and
P
rivacy-preserving
R
eal-time
I
ncentive system for
C
rowds
E
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
three orders of magnitude
compared with other competing schemes.