Securing IoMT healthcare systems with federated learning and BigchainDB

IF 6.2 2区计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS Future Generation Computer Systems-The International Journal of Escience Pub Date : 2024-11-14 DOI:10.1016/j.future.2024.107609

Masoumeh Jafari, Fazlollah Adibnia

引用次数: 0

Abstract

The Internet of Medical Things (IoMT) is transforming healthcare by allowing the storage of patient data for diagnostics and treatment. However, this technology faces significant challenges, including ensuring data reliability, security, quality, and privacy. This study proposes a new architecture that uses Federated Learning (FL) and BigchainDB to address these issues. By using FL and BigchainDB, only authorized and trustworthy devices can store their data in the blockchain. This prevents unauthorized access to the blockchain and its stored data. We evaluated this architecture on a real-world model.

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利用联合学习和 BigchainDB 确保 IoMT 医疗系统的安全

医疗物联网（IoMT）可存储用于诊断和治疗的患者数据，从而改变医疗保健行业。然而，这项技术面临着巨大的挑战，包括确保数据的可靠性、安全性、质量和隐私。本研究提出了一种使用联邦学习（FL）和 BigchainDB 来解决这些问题的新架构。通过使用 Federated Learning 和 BigchainDB，只有经过授权且值得信赖的设备才能将其数据存储到区块链中。这可以防止未经授权访问区块链及其存储的数据。我们在一个真实世界模型上对这一架构进行了评估。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Future Generation Computer Systems-The International Journal of Escience 工程技术-计算机：理论方法

CiteScore

19.90

自引率

2.70%

发文量

376

审稿时长

10.6 months

期刊介绍： Computing infrastructures and systems are constantly evolving, resulting in increasingly complex and collaborative scientific applications. To cope with these advancements, there is a growing need for collaborative tools that can effectively map, control, and execute these applications. Furthermore, with the explosion of Big Data, there is a requirement for innovative methods and infrastructures to collect, analyze, and derive meaningful insights from the vast amount of data generated. This necessitates the integration of computational and storage capabilities, databases, sensors, and human collaboration. Future Generation Computer Systems aims to pioneer advancements in distributed systems, collaborative environments, high-performance computing, and Big Data analytics. It strives to stay at the forefront of developments in grids, clouds, and the Internet of Things (IoT) to effectively address the challenges posed by these wide-area, fully distributed sensing and computing systems.