Boulemtafes Amine, Derhab Abdelouahid, Challal Yacine
Division Sécurité Informatique, Centre de Recherche sur l'Information Scientifique et Technique, Algiers, Algeria, and also Département Informatique, Faculté des Sciences exactes, Université de Bejaia, Bejaia, Algeria.
Center of Excellence in Information Assurance, King Saud University, Riyadh, Saudi Arabia.
Health Technol (Berl). 2022;12(2):285-304. doi: 10.1007/s12553-022-00640-3. Epub 2022 Feb 4.
In recent years, deep learning in healthcare applications has attracted considerable attention from research community. They are deployed on powerful cloud infrastructures to process big health data. However, privacy issue arises when sensitive data are offloaded to the remote cloud. In this paper, we focus on pervasive health monitoring applications that allow anywhere and anytime monitoring of patients, such as heart diseases diagnosis, sleep apnea detection, and more recently, early detection of Covid-19. As pervasive health monitoring applications generally operate on constrained client-side environment, it is important to take into consideration these constraints when designing privacy-preserving solutions. This paper aims therefore to review the adequacy of existing privacy-preserving solutions for deep learning in pervasive health monitoring environment. To this end, we identify the privacy-preserving learning scenarios and their corresponding tasks and requirements. Furthermore, we define the evaluation criteria of the reviewed solutions, we discuss them, and highlight open issues for future research.
近年来，深度学习在医疗保健应用中的应用引起了研究界的广泛关注。它们被部署在强大的云基础设施上，以处理大量的健康数据。然而，当敏感数据被卸载到远程云时，隐私问题就出现了。在本文中，我们关注的是普及型健康监测应用，这些应用允许随时随地对患者进行监测，如心脏病诊断、睡眠呼吸暂停检测，以及最近的新冠病毒-19早期检测。由于普及型健康监测应用通常在受限的客户端环境中运行，因此在设计隐私保护解决方案时考虑这些限制非常重要。因此，本文旨在回顾在普及型健康监测环境中用于深度学习的现有隐私保护解决方案的适用性。为此，我们确定了隐私保护学习场景及其相应的任务和要求。此外，我们定义了所审查解决方案的评估标准，对其进行了讨论，并突出了未来研究的开放性问题。
