医疗物联网中患者医疗数据的安全认证与数据传输

Anup Patnaik, K. Prasad
{"title":"医疗物联网中患者医疗数据的安全认证与数据传输","authors":"Anup Patnaik, K. Prasad","doi":"10.33889/ijmems.2023.8.5.058","DOIUrl":null,"url":null,"abstract":"Currently, data transmission is an expanding area in healthcare, enabling health practitioners to examine, assess, and manage patients using mobile communication technologies. To identify and analyze a patient, healthcare providers need to access the physician's Electronic Medical Record (EMR), which may contain extensive audiovisual big data such as MRIs, CT scans, PET scans, X-rays, and more. To ensure accessibility and scalability for healthcare workers and consumers, the EMR needs to be stored in large data repositories on cloud servers. However, due to the sensitive nature of medical information stored in the cloud, the healthcare profession faces numerous security challenges, with data theft attacks being one of the most critical vulnerabilities. This research focuses on protecting medically sensitive data in the cloud by leveraging cloud computing facilities. The upgraded AES approach ensures that confidential data is securely accessible and stored. In addition, improved Elliptic Curve Cryptography (ECC) is utilized for key generation and validation. A hybrid optimization approach, combining robust optimization and genetic algorithms, is employed to select unique and distinct keys. Decryption is performed using deep neural networks, and Convolutional Neural Networks (CNN) enable batch encryption of multiple documents. The comparison between old methods and the proposed approach is based on encryption time, decryption time, and security strength.","PeriodicalId":44185,"journal":{"name":"International Journal of Mathematical Engineering and Management Sciences","volume":null,"pages":null},"PeriodicalIF":1.3000,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Secure Authentication and Data Transmission for Patients Healthcare Data in Internet of Medical Things\",\"authors\":\"Anup Patnaik, K. Prasad\",\"doi\":\"10.33889/ijmems.2023.8.5.058\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Currently, data transmission is an expanding area in healthcare, enabling health practitioners to examine, assess, and manage patients using mobile communication technologies. To identify and analyze a patient, healthcare providers need to access the physician's Electronic Medical Record (EMR), which may contain extensive audiovisual big data such as MRIs, CT scans, PET scans, X-rays, and more. To ensure accessibility and scalability for healthcare workers and consumers, the EMR needs to be stored in large data repositories on cloud servers. However, due to the sensitive nature of medical information stored in the cloud, the healthcare profession faces numerous security challenges, with data theft attacks being one of the most critical vulnerabilities. This research focuses on protecting medically sensitive data in the cloud by leveraging cloud computing facilities. The upgraded AES approach ensures that confidential data is securely accessible and stored. In addition, improved Elliptic Curve Cryptography (ECC) is utilized for key generation and validation. A hybrid optimization approach, combining robust optimization and genetic algorithms, is employed to select unique and distinct keys. Decryption is performed using deep neural networks, and Convolutional Neural Networks (CNN) enable batch encryption of multiple documents. The comparison between old methods and the proposed approach is based on encryption time, decryption time, and security strength.\",\"PeriodicalId\":44185,\"journal\":{\"name\":\"International Journal of Mathematical Engineering and Management Sciences\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.3000,\"publicationDate\":\"2023-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Mathematical Engineering and Management Sciences\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.33889/ijmems.2023.8.5.058\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ENGINEERING, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Mathematical Engineering and Management Sciences","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.33889/ijmems.2023.8.5.058","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0

摘要

目前,数据传输在医疗保健中是一个不断扩大的领域,使医疗从业者能够使用移动通信技术检查、评估和管理患者。为了识别和分析患者,医疗保健提供者需要访问医生的电子病历(EMR),其中可能包含大量视听大数据,如mri、CT扫描、PET扫描、x射线等。为了确保医疗保健工作者和消费者的可访问性和可伸缩性,EMR需要存储在云服务器上的大型数据存储库中。然而,由于存储在云中医疗信息的敏感性,医疗保健行业面临着许多安全挑战,数据盗窃攻击是最严重的漏洞之一。本研究的重点是利用云计算设施保护云中的医疗敏感数据。升级后的AES方法可确保机密数据的安全访问和存储。此外,采用改进的椭圆曲线加密技术(ECC)进行密钥生成和验证。采用鲁棒优化和遗传算法相结合的混合优化方法选择唯一键和不同键。解密使用深度神经网络执行,卷积神经网络(CNN)支持对多个文档进行批量加密。基于加密时间、解密时间和安全强度对旧方法和新方法进行比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Secure Authentication and Data Transmission for Patients Healthcare Data in Internet of Medical Things
Currently, data transmission is an expanding area in healthcare, enabling health practitioners to examine, assess, and manage patients using mobile communication technologies. To identify and analyze a patient, healthcare providers need to access the physician's Electronic Medical Record (EMR), which may contain extensive audiovisual big data such as MRIs, CT scans, PET scans, X-rays, and more. To ensure accessibility and scalability for healthcare workers and consumers, the EMR needs to be stored in large data repositories on cloud servers. However, due to the sensitive nature of medical information stored in the cloud, the healthcare profession faces numerous security challenges, with data theft attacks being one of the most critical vulnerabilities. This research focuses on protecting medically sensitive data in the cloud by leveraging cloud computing facilities. The upgraded AES approach ensures that confidential data is securely accessible and stored. In addition, improved Elliptic Curve Cryptography (ECC) is utilized for key generation and validation. A hybrid optimization approach, combining robust optimization and genetic algorithms, is employed to select unique and distinct keys. Decryption is performed using deep neural networks, and Convolutional Neural Networks (CNN) enable batch encryption of multiple documents. The comparison between old methods and the proposed approach is based on encryption time, decryption time, and security strength.
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
CiteScore
3.80
自引率
6.20%
发文量
57
审稿时长
20 weeks
期刊介绍: IJMEMS is a peer reviewed international journal aiming on both the theoretical and practical aspects of mathematical, engineering and management sciences. The original, not-previously published, research manuscripts on topics such as the following (but not limited to) will be considered for publication: *Mathematical Sciences- applied mathematics and allied fields, operations research, mathematical statistics. *Engineering Sciences- computer science engineering, mechanical engineering, information technology engineering, civil engineering, aeronautical engineering, industrial engineering, systems engineering, reliability engineering, production engineering. *Management Sciences- engineering management, risk management, business models, supply chain management.
期刊最新文献
Transformation of the Decisional Leadership Role Reliability Analysis of the Functional Capabilities of an Autonomous Vehicle Modeling Developable Surfaces using Quintic Bézier and Hermite Curves Deep Learning based Model for Detection of Vitiligo Skin Disease using Pre-trained Inception V3 A Comparative Study using Scale-2 and Scale-3 Haar Wavelet for the Solution of Higher Order Differential Equation
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1