基于大数据的医疗数据网络安全及隐私保护方法

IF 0.5 4区 计算机科学 Q4 COMPUTER SCIENCE, SOFTWARE ENGINEERING
Jianhong Li, An Pan, Tongxing Zheng
{"title":"基于大数据的医疗数据网络安全及隐私保护方法","authors":"Jianhong Li, An Pan, Tongxing Zheng","doi":"10.4018/ijdwm.325222","DOIUrl":null,"url":null,"abstract":"Big data brings new opportunities to discover the new value of healthcare industry, since it can help us understand the hidden value of data deeply. This also brings new challenges: how to effectively manage and organize these datasets. Throughout the whole life cycle of publishing, storing, mining, and using big data in health care, different users are involved, so there are corresponding privacy protection methods and technologies for different life cycles. Data usage is the last and most important part of the whole life cycle. Therefore, this article proposes a privacy protection method for large medical data: an access control based on credibility of the requesting user. This model evaluates and quantifies doctors' credibility from the perspective of behavioral trust. Comparative experiments show that under the background of linear, geometric and exponential distribution trends and mixed trends, the regression model in this article is better than the existing methods in predicting trust accuracy and trust trends.","PeriodicalId":54963,"journal":{"name":"International Journal of Data Warehousing and Mining","volume":null,"pages":null},"PeriodicalIF":0.5000,"publicationDate":"2023-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Cybersecurity of Medical Data Based on Big Data and Privacy Protection Method\",\"authors\":\"Jianhong Li, An Pan, Tongxing Zheng\",\"doi\":\"10.4018/ijdwm.325222\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Big data brings new opportunities to discover the new value of healthcare industry, since it can help us understand the hidden value of data deeply. This also brings new challenges: how to effectively manage and organize these datasets. Throughout the whole life cycle of publishing, storing, mining, and using big data in health care, different users are involved, so there are corresponding privacy protection methods and technologies for different life cycles. Data usage is the last and most important part of the whole life cycle. Therefore, this article proposes a privacy protection method for large medical data: an access control based on credibility of the requesting user. This model evaluates and quantifies doctors' credibility from the perspective of behavioral trust. Comparative experiments show that under the background of linear, geometric and exponential distribution trends and mixed trends, the regression model in this article is better than the existing methods in predicting trust accuracy and trust trends.\",\"PeriodicalId\":54963,\"journal\":{\"name\":\"International Journal of Data Warehousing and Mining\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.5000,\"publicationDate\":\"2023-06-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Data Warehousing and Mining\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.4018/ijdwm.325222\",\"RegionNum\":4,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"COMPUTER SCIENCE, SOFTWARE ENGINEERING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Data Warehousing and Mining","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.4018/ijdwm.325222","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"COMPUTER SCIENCE, SOFTWARE ENGINEERING","Score":null,"Total":0}
引用次数: 0

摘要

大数据带来了发现医疗行业新价值的新机会,因为它可以帮助我们深入了解数据的隐藏价值。这也带来了新的挑战:如何有效地管理和组织这些数据集。在医疗保健大数据的发布、存储、挖掘和使用的整个生命周期中,涉及到不同的用户,因此针对不同的生命周期有相应的隐私保护方法和技术。数据使用是整个生命周期中最后也是最重要的部分。因此,本文提出了一种针对大型医疗数据的隐私保护方法:基于请求用户可信度的访问控制。该模型从行为信任的角度来评估和量化医生的可信度。对比实验表明,在线性、几何和指数分布趋势以及混合趋势的背景下,本文的回归模型在预测信任准确性和信任趋势方面优于现有方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Cybersecurity of Medical Data Based on Big Data and Privacy Protection Method
Big data brings new opportunities to discover the new value of healthcare industry, since it can help us understand the hidden value of data deeply. This also brings new challenges: how to effectively manage and organize these datasets. Throughout the whole life cycle of publishing, storing, mining, and using big data in health care, different users are involved, so there are corresponding privacy protection methods and technologies for different life cycles. Data usage is the last and most important part of the whole life cycle. Therefore, this article proposes a privacy protection method for large medical data: an access control based on credibility of the requesting user. This model evaluates and quantifies doctors' credibility from the perspective of behavioral trust. Comparative experiments show that under the background of linear, geometric and exponential distribution trends and mixed trends, the regression model in this article is better than the existing methods in predicting trust accuracy and trust trends.
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
International Journal of Data Warehousing and Mining
International Journal of Data Warehousing and Mining COMPUTER SCIENCE, SOFTWARE ENGINEERING-
CiteScore
2.40
自引率
0.00%
发文量
20
审稿时长
>12 weeks
期刊介绍: The International Journal of Data Warehousing and Mining (IJDWM) disseminates the latest international research findings in the areas of data management and analyzation. IJDWM provides a forum for state-of-the-art developments and research, as well as current innovative activities focusing on the integration between the fields of data warehousing and data mining. Emphasizing applicability to real world problems, this journal meets the needs of both academic researchers and practicing IT professionals.The journal is devoted to the publications of high quality papers on theoretical developments and practical applications in data warehousing and data mining. Original research papers, state-of-the-art reviews, and technical notes are invited for publications. The journal accepts paper submission of any work relevant to data warehousing and data mining. Special attention will be given to papers focusing on mining of data from data warehouses; integration of databases, data warehousing, and data mining; and holistic approaches to mining and archiving
×
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