{"title":"具有隐私保护功能的工业设备智能诊断模型","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":"{\"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}","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}
An intelligent diagnostic model for industrial equipment with privacy protection
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.