同态加密实现数字健康的私有数据共享:赢得Vinnova创新竞赛Vinter 2021-22的入场权

Rickard Brännvall, Henrik Forsgren, H. Linge, M. Santini, Alireza Salehi, Fatemeh Rahimian
{"title":"同态加密实现数字健康的私有数据共享:赢得Vinnova创新竞赛Vinter 2021-22的入场权","authors":"Rickard Brännvall, Henrik Forsgren, H. Linge, M. Santini, Alireza Salehi, Fatemeh Rahimian","doi":"10.1109/sais55783.2022.9833062","DOIUrl":null,"url":null,"abstract":"People living with type 1 diabetes often use several apps and devices that help them collect and analyse data for a better monitoring and management of their disease. When such health related data is analysed in the cloud, one must always carefully consider privacy protection and adhere to laws regulating the use of personal data. In this paper we present our experience at the pilot Vinter competition 2021–22 organised by Vinnova. The competition focused on digital services that handle sensitive diabetes related data. The architecture that we proposed for the competition is discussed in the context of a hypothetical cloud-based service that calculates diabetes self-care metrics under strong privacy preservation. It is based on Fully Homomorphic Encryption (FHE) - a technology that makes computation on encrypted data possible. Our solution promotes safe key management and data life-cycle control. Our benchmarking experiment demonstrates execution times that scale well for the implementation of personalised health services. We argue that this technology has great potentials for AI-based health applications and opens up new markets for third-party providers of such services, and will ultimately promote patient health and a trustworthy digital society.","PeriodicalId":228143,"journal":{"name":"2022 Swedish Artificial Intelligence Society Workshop (SAIS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Homomorphic encryption enables private data sharing for digital health: winning entry to the Vinnova innovation competition Vinter 2021–22\",\"authors\":\"Rickard Brännvall, Henrik Forsgren, H. Linge, M. Santini, Alireza Salehi, Fatemeh Rahimian\",\"doi\":\"10.1109/sais55783.2022.9833062\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"People living with type 1 diabetes often use several apps and devices that help them collect and analyse data for a better monitoring and management of their disease. When such health related data is analysed in the cloud, one must always carefully consider privacy protection and adhere to laws regulating the use of personal data. In this paper we present our experience at the pilot Vinter competition 2021–22 organised by Vinnova. The competition focused on digital services that handle sensitive diabetes related data. The architecture that we proposed for the competition is discussed in the context of a hypothetical cloud-based service that calculates diabetes self-care metrics under strong privacy preservation. It is based on Fully Homomorphic Encryption (FHE) - a technology that makes computation on encrypted data possible. Our solution promotes safe key management and data life-cycle control. Our benchmarking experiment demonstrates execution times that scale well for the implementation of personalised health services. We argue that this technology has great potentials for AI-based health applications and opens up new markets for third-party providers of such services, and will ultimately promote patient health and a trustworthy digital society.\",\"PeriodicalId\":228143,\"journal\":{\"name\":\"2022 Swedish Artificial Intelligence Society Workshop (SAIS)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-06-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 Swedish Artificial Intelligence Society Workshop (SAIS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/sais55783.2022.9833062\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 Swedish Artificial Intelligence Society Workshop (SAIS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/sais55783.2022.9833062","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5

摘要

1型糖尿病患者经常使用几个应用程序和设备来帮助他们收集和分析数据,以便更好地监测和管理他们的疾病。在云中分析此类健康相关数据时,必须始终仔细考虑隐私保护问题,并遵守规范个人数据使用的法律。在本文中,我们介绍了我们在Vinnova组织的2021-22试点Vinter竞赛中的经验。竞争的重点是处理敏感糖尿病相关数据的数字服务。我们为竞赛提出的架构是在假设的基于云的服务的背景下讨论的,该服务可以在强隐私保护的情况下计算糖尿病自我保健指标。它基于完全同态加密(FHE)——一种使加密数据的计算成为可能的技术。我们的解决方案促进了安全的密钥管理和数据生命周期控制。我们的基准测试实验表明,执行时间可以很好地扩展到个性化医疗服务的实施。我们认为,这项技术在基于人工智能的健康应用方面具有巨大的潜力,并为此类服务的第三方提供商开辟了新的市场,最终将促进患者健康和可信赖的数字社会。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Homomorphic encryption enables private data sharing for digital health: winning entry to the Vinnova innovation competition Vinter 2021–22
People living with type 1 diabetes often use several apps and devices that help them collect and analyse data for a better monitoring and management of their disease. When such health related data is analysed in the cloud, one must always carefully consider privacy protection and adhere to laws regulating the use of personal data. In this paper we present our experience at the pilot Vinter competition 2021–22 organised by Vinnova. The competition focused on digital services that handle sensitive diabetes related data. The architecture that we proposed for the competition is discussed in the context of a hypothetical cloud-based service that calculates diabetes self-care metrics under strong privacy preservation. It is based on Fully Homomorphic Encryption (FHE) - a technology that makes computation on encrypted data possible. Our solution promotes safe key management and data life-cycle control. Our benchmarking experiment demonstrates execution times that scale well for the implementation of personalised health services. We argue that this technology has great potentials for AI-based health applications and opens up new markets for third-party providers of such services, and will ultimately promote patient health and a trustworthy digital society.
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
自引率
0.00%
发文量
0
×
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