LPF-IVN:具有智能车联网功能机制的轻量级隐私增强方案

IF 6 3区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
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 ,&nbsp;Weijin Jiang ,&nbsp;Yirong Jiang ,&nbsp;Yixiao Li ,&nbsp;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":"28 ","pages":"Article 101400"},"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 ,&nbsp;Weijin Jiang ,&nbsp;Yirong Jiang ,&nbsp;Yixiao Li ,&nbsp;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\":\"28 \",\"pages\":\"Article 101400\"},\"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}
引用次数: 0

摘要

由于去中心化和有效防止隐私泄露,差分私有联合学习(DP-FL)已成为车联网(IoV)中的一种有效技术。然而,关键产业的本质是大数据。将DP-FL模型应用于车联网时,这些非IID、高维等大规模非轻量级数据会降低模型的安全性和准确性。因此,考虑到物联网背景下异构和隐私泄露的影响,为了物联网的安全性和准确性,我们提出了一种名为 DPF-IVN 的轻量级 DP-FL 框架。它采用 "先降维、后优化 "的思路来处理物联网中的非轻量化数据。具体来说,我们新颖地设计了一种联邦随机主成分分析(Federated Randomized Principal Component Analysis,FRPCA)算法,允许用户将本地数据映射为低维数据。然后,我们提出了扰乱梯度参数的功能机制(FM),以解决梯度切割导致的训练精度低的问题。此外,为了减少模型差异,我们使用 Bregman 分散作为正则化项更新损失函数,以提高模型的准确性。大量的实验证明,与其他方法相比,DPF-IVN 在异构环境中的性能更加优越。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Internet of Things
Internet of Things Multiple-
CiteScore
3.60
自引率
5.10%
发文量
115
审稿时长
37 days
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.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信