{"title":"An intelligent diagnostic model for industrial equipment with privacy protection","authors":"","doi":"10.1016/j.cose.2024.104036","DOIUrl":null,"url":null,"abstract":"<div><p>Intelligent diagnostic modeling of industrial equipment (IDMIE) addresses various industrial challenges, yet concerns about data privacy security have been raised by many organizations. However, the reliance on third-party trust and the stringent privacy requirements pose obstacles to ensuring privacy. To tackle these issues, this study proposes a generative model based on the framework of differential privacy and one-dimensional operational generative adversarial networks (DP1D-OpGAN), in which, in order to reduce the privacy budget and ensure the privacy of the generative model, a method involving training the learning parameters with perturbed gradient vectors is proposed. Additionally, the classification model of discrete multi-wavelet transforms convolutional neural network (DMWA-CNN) is integrated to enhance the diagnostic performance of the model. The model's safety, high performance, and generalizability are validated through multiple comprehensive experiments.</p></div>","PeriodicalId":51004,"journal":{"name":"Computers & Security","volume":null,"pages":null},"PeriodicalIF":4.8000,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computers & Security","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0167404824003419","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
Intelligent diagnostic modeling of industrial equipment (IDMIE) addresses various industrial challenges, yet concerns about data privacy security have been raised by many organizations. However, the reliance on third-party trust and the stringent privacy requirements pose obstacles to ensuring privacy. To tackle these issues, this study proposes a generative model based on the framework of differential privacy and one-dimensional operational generative adversarial networks (DP1D-OpGAN), in which, in order to reduce the privacy budget and ensure the privacy of the generative model, a method involving training the learning parameters with perturbed gradient vectors is proposed. Additionally, the classification model of discrete multi-wavelet transforms convolutional neural network (DMWA-CNN) is integrated to enhance the diagnostic performance of the model. The model's safety, high performance, and generalizability are validated through multiple comprehensive experiments.
期刊介绍:
Computers & Security is the most respected technical journal in the IT security field. With its high-profile editorial board and informative regular features and columns, the journal is essential reading for IT security professionals around the world.
Computers & Security provides you with a unique blend of leading edge research and sound practical management advice. It is aimed at the professional involved with computer security, audit, control and data integrity in all sectors - industry, commerce and academia. Recognized worldwide as THE primary source of reference for applied research and technical expertise it is your first step to fully secure systems.