Privacy-Preserving Learning Model Using Lightweight Encryption for Visual Sensing Industrial IoT Devices

Abstract

Technological convergence in visual sensing with industrial IoT (VSI-IoT) can bring numerous advances to large-scale crowd management systems like visual crowdsensing. VSI-IoT has significant features, including sensing, computing, analyzing, and storing, to address the issues of bearing failures, such as unplanned outages, increased downtime, and reduced operational efficiency. By contrast, providing privacy to the IIoT environments is a challenging task. Thus, this paper presents a novel privacy-preserving learning (PPL) mechanism that senses the defect rate of bearing failures using lightweight model aggregation at edge computing systems to preserve the privacy features. This convergence model synthesizes shape features comprehensively to transform the feature vectors into predictive functions that examine the categorization models using a two-dimensional convolution neural network (2D-CNN). Using security analysis, we demonstrate that the proposed PPL can achieve better privacy protection and model accuracy to preserve the learning features without additional verifiability. Further, the examination results showed that the proposed 2D-CNN with BN and LN consumed less computation complexity to achieve better detection accuracy $(\approx 87.91.9\%$ to $\approx 99.98\%)$ and communication cost $ (\approx 21.09MB$ to $ 23.92MB)$ over three bearing datasets (i.e., IMS-Rexnord, CWRU, and Paderborn) than other state-of-the-art approaches. Above all, the privacy preserving based AlexNet was implemented using CryptoNet and LoLa to show different sets of efficiencies such as processing time, privacy, and integrity checks to preserve system performance following time-sensitive application scenarios like supply-chain optimization.