Zero watermarking scheme for privacy protection in e-Health care

IF 1.7 4区 计算机科学 Q3 AUTOMATION & CONTROL SYSTEMS Automatika Pub Date : 2023-03-18 DOI:10.1080/00051144.2023.2187525
Ayesha Shaik, V. Masilamani
{"title":"Zero watermarking scheme for privacy protection in e-Health care","authors":"Ayesha Shaik, V. Masilamani","doi":"10.1080/00051144.2023.2187525","DOIUrl":null,"url":null,"abstract":"E-health care is an emerging field where health services and information are delivered and offered over the Internet. So the health information of the patients communicated over the Internet has to protect the privacy of the patients. The patient information is embedded into the health record and communicated online which also induces degradation to the original information. So, in this article, a zero watermarking scheme for privacy protection is proposed which protects the privacy and also eliminates the degradation done during embedding of patient information into the health record. This method is based on simple linear iterative clustering (SLIC) superpixels and partial pivoting lower triangular upper triangular (PPLU) factorization. The novelty of this article is that the use of SLIC superpixels and PPLU decomposition for the privacy protection of medical images (MI). The original image is subjected to SLIC segmentation and non-overlapping high entropy blocks are selected. On the selected blocks discrete wavelet transform (DWT) is applied and those blocks undergo PPLU factorization to get three matrices, L, U and P, which are lower triangular, upper triangular and permutation matrix respectively. The product matrix is used to construct a zero-watermark. The technique has been experimented on the UCID, BOWS and SIPI databases. The test results demonstrate that this work shows high robustness which is measured using normalized correlation (NC) and bit error rate (BER) against the listed attacks.","PeriodicalId":55412,"journal":{"name":"Automatika","volume":"64 1","pages":"453 - 466"},"PeriodicalIF":1.7000,"publicationDate":"2023-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Automatika","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1080/00051144.2023.2187525","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
引用次数: 0

Abstract

E-health care is an emerging field where health services and information are delivered and offered over the Internet. So the health information of the patients communicated over the Internet has to protect the privacy of the patients. The patient information is embedded into the health record and communicated online which also induces degradation to the original information. So, in this article, a zero watermarking scheme for privacy protection is proposed which protects the privacy and also eliminates the degradation done during embedding of patient information into the health record. This method is based on simple linear iterative clustering (SLIC) superpixels and partial pivoting lower triangular upper triangular (PPLU) factorization. The novelty of this article is that the use of SLIC superpixels and PPLU decomposition for the privacy protection of medical images (MI). The original image is subjected to SLIC segmentation and non-overlapping high entropy blocks are selected. On the selected blocks discrete wavelet transform (DWT) is applied and those blocks undergo PPLU factorization to get three matrices, L, U and P, which are lower triangular, upper triangular and permutation matrix respectively. The product matrix is used to construct a zero-watermark. The technique has been experimented on the UCID, BOWS and SIPI databases. The test results demonstrate that this work shows high robustness which is measured using normalized correlation (NC) and bit error rate (BER) against the listed attacks.
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
用于电子医疗隐私保护的零水印方案
电子卫生保健是一个新兴领域,其中卫生服务和信息是通过因特网提供的。因此,通过互联网交流患者的健康信息必须保护患者的隐私。患者信息被嵌入到健康记录中并在线交流,这也会导致原始信息的退化。因此,本文提出了一种用于隐私保护的零水印方案,该方案既保护了隐私,又消除了在健康记录中嵌入患者信息时所造成的退化。该方法基于简单线性迭代聚类(SLIC)超像素和部分枢轴下三角上三角(PPLU)分解。本文的新颖之处在于将SLIC超像素和PPLU分解用于医学图像的隐私保护(MI)。对原始图像进行SLIC分割,选择不重叠的高熵块。对选取的分块进行离散小波变换(DWT),对分块进行PPLU分解,得到L、U、P三个矩阵,分别为下三角矩阵、上三角矩阵和置换矩阵。利用积矩阵构造零水印。该技术已在UCID、BOWS和SIPI数据库上进行了试验。测试结果表明,采用归一化相关(NC)和误码率(BER)对所列攻击具有较高的鲁棒性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Automatika
Automatika AUTOMATION & CONTROL SYSTEMS-ENGINEERING, ELECTRICAL & ELECTRONIC
CiteScore
4.00
自引率
5.30%
发文量
65
审稿时长
4.5 months
期刊介绍: AUTOMATIKA – Journal for Control, Measurement, Electronics, Computing and Communications is an international scientific journal that publishes scientific and professional papers in the field of automatic control, robotics, measurements, electronics, computing, communications and related areas. Click here for full Focus & Scope. AUTOMATIKA is published since 1960, and since 1991 by KoREMA - Croatian Society for Communications, Computing, Electronics, Measurement and Control, Member of IMEKO and IFAC.
期刊最新文献
Robust synchronization of four-dimensional chaotic finance systems with unknown parametric uncertainties Segmenting and classifying skin lesions using a fruit fly optimization algorithm with a machine learning framework An implementation of inertia control strategy for grid-connected solar system using moth-flame optimization algorithm Empowering diagnosis: an astonishing deep transfer learning approach with fine tuning for precise lung disease classification from CXR images A comparative analysis: optimal node selection in large data block transmission in VANET using various node relay optimization algorithms
×
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