Recently, many innovations have been experienced in healthcare by rapidly growing Internet-of-Things (IoT) technology that provides significant developments and facilities in the health sector and improves daily human life. The IoT bridges people, information technology and speed up shopping. For these reasons, IoT technology has started to be used on a large scale. Thanks to the use of IoT technology in health services, chronic disease monitoring, health monitoring, rapid intervention, early diagnosis and treatment, etc. facilitates the delivery of health services. However, the data transferred to the digital environment pose a threat of privacy leakage. Unauthorized persons have used them, and there have been malicious attacks on the health and privacy of individuals. In this study, it is aimed to propose a model to handle the privacy problems based on federated learning. Besides, we apply secure multi party computation. Our proposed model presents an extensive privacy and data analysis and achieve high performance.
翻译:最近,在保健方面,通过迅速增长的互联网技术(IoT)在保健方面取得了许多创新,这些技术为卫生部门提供了重要的发展和设施,并改善了日常生活;IoT连接了人们、信息技术和加快购物速度;由于这些原因,IoT技术已开始大规模使用;由于在保健服务、慢性疾病监测、健康监测、快速干预、早期诊断和治疗等方面使用IoT技术,便利了保健服务的提供;然而,转移到数字环境的数据有隐私泄漏的威胁;未经授权的人利用了这些数据,对个人的健康和隐私进行了恶意攻击;这项研究旨在提出一种模式,处理基于联邦学习的隐私问题;此外,我们采用安全的多党计算方法;我们提议的模型提供了广泛的隐私和数据分析,并取得了高绩效。