SecureNet:基于深度学习的医疗保健数据安全框架

IF 4.9 3区 计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE
Vishnu Bharadwaj Bayari Parkala , Gaurav Bhatnagar , Chiranjoy Chattopadhyay
{"title":"SecureNet:基于深度学习的医疗保健数据安全框架","authors":"Vishnu Bharadwaj Bayari Parkala ,&nbsp;Gaurav Bhatnagar ,&nbsp;Chiranjoy Chattopadhyay","doi":"10.1016/j.compeleceng.2025.110723","DOIUrl":null,"url":null,"abstract":"<div><div>As medical devices become increasingly interconnected through the Internet of Medical Things (IoMT), safeguarding image data against unauthorized access and tampering has become a pressing challenge. Many existing solutions fall short in balancing computational efficiency with robust encryption, particularly when high-fidelity recovery of diagnostic images is required. This work presents SecureNet, a hybrid encryption framework tailored for medical imaging applications. This work proposes SecureNet, a compact and resilient encryption framework designed for secure medical image transmission. SecureNet leverages a convolutional autoencoder to extract compact latent features, applies spatial rearrangement using a Hilbert curve to disrupt pixel locality, and diffusion with a chaos-driven random projection. The result is a highly randomized and distorted representation, complicating any attempt at unauthorized analysis. The encrypted data can be accurately recovered through symmetric decryption operations. Experimental results, supported by security and comparative analyses, validate the effectiveness and generalizability of the proposed framework in resisting various attacks, demonstrating its encryption efficacy and performance on par with state-of-the-art approaches. The framework presents a scalable solution for securing healthcare data in dynamic and resource-constrained IoMT environments.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"128 ","pages":"Article 110723"},"PeriodicalIF":4.9000,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"SecureNet: A deep learning inspired security framework for healthcare data\",\"authors\":\"Vishnu Bharadwaj Bayari Parkala ,&nbsp;Gaurav Bhatnagar ,&nbsp;Chiranjoy Chattopadhyay\",\"doi\":\"10.1016/j.compeleceng.2025.110723\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>As medical devices become increasingly interconnected through the Internet of Medical Things (IoMT), safeguarding image data against unauthorized access and tampering has become a pressing challenge. Many existing solutions fall short in balancing computational efficiency with robust encryption, particularly when high-fidelity recovery of diagnostic images is required. This work presents SecureNet, a hybrid encryption framework tailored for medical imaging applications. This work proposes SecureNet, a compact and resilient encryption framework designed for secure medical image transmission. SecureNet leverages a convolutional autoencoder to extract compact latent features, applies spatial rearrangement using a Hilbert curve to disrupt pixel locality, and diffusion with a chaos-driven random projection. The result is a highly randomized and distorted representation, complicating any attempt at unauthorized analysis. The encrypted data can be accurately recovered through symmetric decryption operations. Experimental results, supported by security and comparative analyses, validate the effectiveness and generalizability of the proposed framework in resisting various attacks, demonstrating its encryption efficacy and performance on par with state-of-the-art approaches. The framework presents a scalable solution for securing healthcare data in dynamic and resource-constrained IoMT environments.</div></div>\",\"PeriodicalId\":50630,\"journal\":{\"name\":\"Computers & Electrical Engineering\",\"volume\":\"128 \",\"pages\":\"Article 110723\"},\"PeriodicalIF\":4.9000,\"publicationDate\":\"2025-09-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Computers & Electrical Engineering\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0045790625006664\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, HARDWARE & ARCHITECTURE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computers & Electrical Engineering","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0045790625006664","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, HARDWARE & ARCHITECTURE","Score":null,"Total":0}
引用次数: 0

摘要

随着医疗设备通过医疗物联网(IoMT)日益互联,保护图像数据免受未经授权的访问和篡改已成为一项紧迫的挑战。许多现有的解决方案在平衡计算效率和健壮的加密方面存在不足,特别是在需要高保真恢复诊断图像时。这项工作提出了SecureNet,一个为医学成像应用量身定制的混合加密框架。这项工作提出了SecureNet,一个紧凑和弹性的加密框架,设计用于安全的医学图像传输。SecureNet利用卷积自编码器提取紧凑的潜在特征,使用希尔伯特曲线应用空间重排来破坏像素局部性,并使用混沌驱动的随机投影进行扩散。结果是一个高度随机和扭曲的表示,使任何未经授权的分析尝试复杂化。通过对称解密操作,可以准确恢复加密后的数据。在安全性和比较分析的支持下,实验结果验证了所提出框架在抵抗各种攻击方面的有效性和通用性,证明了其加密效率和性能与最先进的方法相当。该框架提供了一个可扩展的解决方案,用于在动态和资源受限的IoMT环境中保护医疗保健数据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
SecureNet: A deep learning inspired security framework for healthcare data
As medical devices become increasingly interconnected through the Internet of Medical Things (IoMT), safeguarding image data against unauthorized access and tampering has become a pressing challenge. Many existing solutions fall short in balancing computational efficiency with robust encryption, particularly when high-fidelity recovery of diagnostic images is required. This work presents SecureNet, a hybrid encryption framework tailored for medical imaging applications. This work proposes SecureNet, a compact and resilient encryption framework designed for secure medical image transmission. SecureNet leverages a convolutional autoencoder to extract compact latent features, applies spatial rearrangement using a Hilbert curve to disrupt pixel locality, and diffusion with a chaos-driven random projection. The result is a highly randomized and distorted representation, complicating any attempt at unauthorized analysis. The encrypted data can be accurately recovered through symmetric decryption operations. Experimental results, supported by security and comparative analyses, validate the effectiveness and generalizability of the proposed framework in resisting various attacks, demonstrating its encryption efficacy and performance on par with state-of-the-art approaches. The framework presents a scalable solution for securing healthcare data in dynamic and resource-constrained IoMT environments.
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
Computers & Electrical Engineering
Computers & Electrical Engineering 工程技术-工程:电子与电气
CiteScore
9.20
自引率
7.00%
发文量
661
审稿时长
47 days
期刊介绍: The impact of computers has nowhere been more revolutionary than in electrical engineering. The design, analysis, and operation of electrical and electronic systems are now dominated by computers, a transformation that has been motivated by the natural ease of interface between computers and electrical systems, and the promise of spectacular improvements in speed and efficiency. Published since 1973, Computers & Electrical Engineering provides rapid publication of topical research into the integration of computer technology and computational techniques with electrical and electronic systems. The journal publishes papers featuring novel implementations of computers and computational techniques in areas like signal and image processing, high-performance computing, parallel processing, and communications. Special attention will be paid to papers describing innovative architectures, algorithms, and software tools.
×
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:604180095
Book学术官方微信