R‐DP: A risk‐adaptive privacy protection scheme for mobile crowdsensing in industrial internet of things

Abstract

Abstract 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‐DP for MCS‐enabled IIoT gadgets is proposed, which comprises a closed‐loop risk‐awareness process and an adaptive privacy protection method DP (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 decision‐makers for the task. In addition, DP 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‐DP 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.

Keywords

• closed loop systems
• computational complexity
• data privacy
• mobile computing
• production engineering computing
• statistical analysis