Haijuan Wang , Weijin Jiang , Yirong Jiang , Yixiao Li , Yusheng Xu
{"title":"LPF-IVN:具有智能车联网功能机制的轻量级隐私增强方案","authors":"Haijuan Wang , Weijin Jiang , Yirong Jiang , Yixiao Li , Yusheng Xu","doi":"10.1016/j.iot.2024.101400","DOIUrl":null,"url":null,"abstract":"<div><div>Due to decentralization and effective prevention of privacy leakage, Differential Private Federated Learning(DP-FL) has emerged as an efficient technique in the Internet of Vehicles (IoV). However, the essence of key industrial is big data. When applying the DP-FL model to the IoV, these large-scale nonlightweight data such as Non-IID and high-dimensional will decrease the security and accuracy of the model. Therefore, for the security and accuracy of the IoV, we proposed a lightweight DP-FL framework called DPF-IVN, considering the impact of heterogeneous and privacy leak in the context of IoV. It adopts the idea of “lowering dimension first and then optimization” to process non-light quantified data in the IoV. Specifically, we novelly design a Federated Randomized Principal Component Analysis (FRPCA) algorithm, allowing users to map local data to low-dimensional data. Then, we propose the Functional Mechanism(FM) to disturb the gradient parameters to solve the problem of low training accuracy caused by gradient cutting. Besides, to reduce model differences, we used the Bregman dispersal as a regularized item update loss function to improve the accuracy of the model. Extensive experiments demonstrate the superior performance of DPF-IVN in the heterogeneous environment compared to other methods.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":null,"pages":null},"PeriodicalIF":6.0000,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"LPF-IVN: A lightweight privacy-enhancing scheme with functional mechanism of intelligent vehicle networking\",\"authors\":\"Haijuan Wang , Weijin Jiang , Yirong Jiang , Yixiao Li , Yusheng Xu\",\"doi\":\"10.1016/j.iot.2024.101400\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Due to decentralization and effective prevention of privacy leakage, Differential Private Federated Learning(DP-FL) has emerged as an efficient technique in the Internet of Vehicles (IoV). However, the essence of key industrial is big data. When applying the DP-FL model to the IoV, these large-scale nonlightweight data such as Non-IID and high-dimensional will decrease the security and accuracy of the model. Therefore, for the security and accuracy of the IoV, we proposed a lightweight DP-FL framework called DPF-IVN, considering the impact of heterogeneous and privacy leak in the context of IoV. It adopts the idea of “lowering dimension first and then optimization” to process non-light quantified data in the IoV. Specifically, we novelly design a Federated Randomized Principal Component Analysis (FRPCA) algorithm, allowing users to map local data to low-dimensional data. Then, we propose the Functional Mechanism(FM) to disturb the gradient parameters to solve the problem of low training accuracy caused by gradient cutting. Besides, to reduce model differences, we used the Bregman dispersal as a regularized item update loss function to improve the accuracy of the model. Extensive experiments demonstrate the superior performance of DPF-IVN in the heterogeneous environment compared to other methods.</div></div>\",\"PeriodicalId\":29968,\"journal\":{\"name\":\"Internet of Things\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":6.0000,\"publicationDate\":\"2024-10-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Internet of Things\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S254266052400341X\",\"RegionNum\":3,\"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":"Internet of Things","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S254266052400341X","RegionNum":3,"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}
LPF-IVN: A lightweight privacy-enhancing scheme with functional mechanism of intelligent vehicle networking
Due to decentralization and effective prevention of privacy leakage, Differential Private Federated Learning(DP-FL) has emerged as an efficient technique in the Internet of Vehicles (IoV). However, the essence of key industrial is big data. When applying the DP-FL model to the IoV, these large-scale nonlightweight data such as Non-IID and high-dimensional will decrease the security and accuracy of the model. Therefore, for the security and accuracy of the IoV, we proposed a lightweight DP-FL framework called DPF-IVN, considering the impact of heterogeneous and privacy leak in the context of IoV. It adopts the idea of “lowering dimension first and then optimization” to process non-light quantified data in the IoV. Specifically, we novelly design a Federated Randomized Principal Component Analysis (FRPCA) algorithm, allowing users to map local data to low-dimensional data. Then, we propose the Functional Mechanism(FM) to disturb the gradient parameters to solve the problem of low training accuracy caused by gradient cutting. Besides, to reduce model differences, we used the Bregman dispersal as a regularized item update loss function to improve the accuracy of the model. Extensive experiments demonstrate the superior performance of DPF-IVN in the heterogeneous environment compared to other methods.
期刊介绍:
Internet of Things; Engineering Cyber Physical Human Systems is a comprehensive journal encouraging cross collaboration between researchers, engineers and practitioners in the field of IoT & Cyber Physical Human Systems. The journal offers a unique platform to exchange scientific information on the entire breadth of technology, science, and societal applications of the IoT.
The journal will place a high priority on timely publication, and provide a home for high quality.
Furthermore, IOT is interested in publishing topical Special Issues on any aspect of IOT.