Privacy protection scheme for edge computing based on function encryption

Yuanhang He, Lei Chen, Yi-Tao Ni, Yuyao Wang, Jiangtao Li, Yufeng Li
{"title":"Privacy protection scheme for edge computing based on function encryption","authors":"Yuanhang He, Lei Chen, Yi-Tao Ni, Yuyao Wang, Jiangtao Li, Yufeng Li","doi":"10.1109/NaNA53684.2021.00030","DOIUrl":null,"url":null,"abstract":"In the response to COVID-19, the big data can achieve a new data application mode with reduced manpower and material costs, which will help the government understand the supply and demand status of medical supplies and make precise decisions to ensure the supply of materials and prevent the spread of the epidemic. However, in the medical big data solution based on cloud computing, the user data storage and computing process are all carried out in the cloud, the risk of medical data is increasingly exposed, and the privacy protection of medical data is worrying. Edge computing makes data privacy protection more operational. Because the data collection and calculation are based on the local, and no longer need to be transmitted to the cloud, some important information, especially sensitive information, cannot be transmitted through the network, effectively solving the problem of user privacy leakage and data security. Function encryption provides data with a very flexible method that meets data confidentiality and effective access control. So based on function encryption, this paper proposes a privacy protection scheme under the edge computing paradigm, introduce the data interaction process for the scheme and demonstrates the privacy protection ability and computing efficiency of the scheme in the main scenario of smart medical care.","PeriodicalId":414672,"journal":{"name":"2021 International Conference on Networking and Network Applications (NaNA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 International Conference on Networking and Network Applications (NaNA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/NaNA53684.2021.00030","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

In the response to COVID-19, the big data can achieve a new data application mode with reduced manpower and material costs, which will help the government understand the supply and demand status of medical supplies and make precise decisions to ensure the supply of materials and prevent the spread of the epidemic. However, in the medical big data solution based on cloud computing, the user data storage and computing process are all carried out in the cloud, the risk of medical data is increasingly exposed, and the privacy protection of medical data is worrying. Edge computing makes data privacy protection more operational. Because the data collection and calculation are based on the local, and no longer need to be transmitted to the cloud, some important information, especially sensitive information, cannot be transmitted through the network, effectively solving the problem of user privacy leakage and data security. Function encryption provides data with a very flexible method that meets data confidentiality and effective access control. So based on function encryption, this paper proposes a privacy protection scheme under the edge computing paradigm, introduce the data interaction process for the scheme and demonstrates the privacy protection ability and computing efficiency of the scheme in the main scenario of smart medical care.
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
基于函数加密的边缘计算隐私保护方案
在应对COVID-19中,大数据可以实现一种新的数据应用模式,减少人力和物力成本,有助于政府了解医疗用品的供需状况,做出准确决策,确保物资供应,防止疫情蔓延。然而,在基于云计算的医疗大数据解决方案中,用户数据的存储和计算过程都在云端进行,医疗数据的风险日益暴露,医疗数据的隐私保护令人担忧。边缘计算使数据隐私保护更具操作性。由于数据采集和计算都是基于本地,不再需要传输到云端,一些重要信息特别是敏感信息无法通过网络传输,有效解决了用户隐私泄露和数据安全问题。功能加密为数据的保密性和有效的访问控制提供了一种非常灵活的方法。因此,本文提出了一种基于功能加密的边缘计算范式下的隐私保护方案,介绍了该方案的数据交互过程,并在智能医疗主要场景下演示了该方案的隐私保护能力和计算效率。
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
自引率
0.00%
发文量
0
期刊最新文献
Covert Communication in D2D Underlaying Cellular Network Online Scheduling of Machine Learning Jobs in Edge-Cloud Networks Dual attention mechanism object tracking algorithm based on Fully-convolutional Siamese network Fatigue Detection Technology for Online Learning The Nearest Neighbor Algorithm for Balanced and Connected k-Center Problem under Modular Distance
×
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