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The integration of the Mobile Crowdsensing (MCS) and Industrial Internet of Things (IIoT) brings enormous volumes of data that generate significant commercial value. However, the data contain a wealth of sensitive information about devices' environmental situation and collective activities, which draws a flock of adversaries and poses an unprecedented security challenge. Furthermore, sensing gadgets deployed in the IIoT device are usually resource-constrained and often do not have adequate 3C resources (i.e. communication, computing, caching) to run sophisticated privacy-preserving methods, making them easier targets for attacks in data sharing. Therefore, a risk-adaptive privacy protection scheme R-D P for MCS-enabled IIoT gadgets is proposed, which comprises a closed-loop risk-awareness process and an adaptive privacy protection method D P (a dissemination process with perturbation). The closed-loop process dynamic awareness of risks and threats in MCS task feeds appropriate privacy protection advice to the decisionmakers for the task. In addition, D P was designed as a lightweight and risk-adaptive privacy protection method to meet the operational needs of 3C resource-constrained gadgets. The analysis and evaluation show that R-D P provides satisfactory privacy protection while the availability of statistical features reaches more than 96%, and the time complexity is only O (1) for sensing gadgets.

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