【Author】
Polap, Dawid; Srivastava, Gautam; Jolfaei, Alireza; Parizi, Reza M.
【Source】IEEE INFOCOM 2020 - IEEE CONFERENCE ON COMPUTER COMMUNICATIONS WORKSHOPS (INFOCOM WKSHPS)
【Abstract】In today's technological climate, users require fast automation and digitization of results for large amounts of data at record speeds. Especially in the field of medicine, where each patient is often asked to undergo many different examinations within one diagnosis or treatment. Each examination can help in the diagnosis or prediction of further disease progression. Furthermore, all produced data from these examinations must be stored somewhere and available to various medical practitioners for analysis who may be in geographically diverse locations. The current medical climate leans towards remote patient monitoring and AI-assisted diagnosis. To make this possible, medical data should ideally be secured and made accessible to many medical practitioners, which makes them prone to malicious entities. Medical information has inherent value to malicious entities due to its privacy-sensitive nature in a variety of ways. Furthermore, if access to data is distributively made available to AI algorithms (particularly neural networks) for further analysis/diagnosis, the danger to the data may increase (e.g., model poisoning with fake data introduction). In this paper, we propose a federated learning approach that uses decentralized learning with blockchain-based security and a proposition that accompanies that training intelligent systems using distributed and locally-stored data for the use of all patients. Our work in progress hopes to contribute to the latest trend of the Internet of Medical Things security and privacy.
【Keywords】Neural Networks; Federated Learning; Internet of Things; Internet of Medical Things; Blockchain; Patient Data; Security; Privacy
【摘要】在当今的技术环境中，用户需要以创纪录的速度对大量数据的结果进行快速自动化和数字化。特别是在医学领域，通常要求每位患者在一次诊断或治疗中接受许多不同的检查。每次检查都有助于诊断或预测进一步的疾病进展。此外，这些检查产生的所有数据都必须存储在某个地方，并可供可能位于不同地理位置的各种医疗从业者进行分析。当前的医疗环境倾向于远程患者监测和人工智能辅助诊断。为了实现这一点，理想情况下，医疗数据应该得到保护，并可供许多医疗从业者访问，这使得他们容易受到恶意实体的攻击。医疗信息对恶意实体具有内在价值，因为它具有多种方式的隐私敏感性。此外，如果人工智能算法（尤其是神经网络）可以分布式地访问数据以进行进一步分析/诊断，则数据的危险可能会增加（例如，引入虚假数据的模型中毒）。在本文中，我们提出了一种联邦学习方法，该方法使用分散式学习和基于区块链的安全性，以及伴随使用分布式和本地存储数据训练智能系统以供所有患者使用的提议。我们正在进行的工作希望为医疗物联网安全和隐私的最新趋势做出贡献。
【关键词】神经网络;联邦学习；物联网;医疗物联网；区块链；患者数据；安全;隐私
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