【Author】
Unal, Devrim; Hammoudeh, Mohammad; Khan, Muhammad Asif; Abuarqoub, Abdelrahman; Epiphaniou, Gregory; Hamila, Ridha
【Source】COMPUTERS & SECURITY
【Abstract】Big data enables the optimization of complex supply chains through Machine Learning (ML) based data analytics. However, data analytics comes with challenges such as the loss of control and privacy leading to increased risk of data breaches. Federated Learning (FL) is an approach in the ML arena that promises privacy-preserving and distributed model training. However, recent attacks on FL algorithms have raised concerns about the security of this approach. In this article, we advocate using Blockchain to mitigate attacks on FL algorithms operating in Internet of Things (IoT) systems. Integrating Blockchain and FL allows securing the trained models' integrity, thus preventing model poisoning attacks. This research presents a practical approach for the integration of Blockchain with FL to provide privacy preserving and secure big data analytics services. To protect the security of user data and the trained models, we propose utilizing fuzzy hashing to detect variations and anomalies in FL-trained models against poisoning attacks. The proposed solution is evaluated via simulating attack modes in a quasi-simulated environment. (c) 2021 Elsevier Ltd. All rights reserved.
【Keywords】Federated learning; Blockchain; Big data; Edge computing; IoT
【标题】联邦机器学习和区块链的集成，为物联网提供安全的大数据分析
【摘要】大数据可以通过基于机器学习 (ML) 的数据分析来优化复杂的供应链。然而，数据分析带来了挑战，例如失去控制和隐私，导致数据泄露风险增加。联邦学习 (FL) 是 ML 领域的一种方法，它承诺保护隐私和分布式模型训练。然而，最近对 FL 算法的攻击引起了人们对这种方法的安全性的担忧。在本文中，我们提倡使用区块链来减轻对在物联网 (IoT) 系统中运行的 FL 算法的攻击。集成区块链和 FL 可以确保训练模型的完整性，从而防止模型中毒攻击。本研究提出了一种将区块链与 FL 集成以提供隐私保护和安全大数据分析服务的实用方法。为了保护用户数据和训练模型的安全性，我们建议利用模糊散列来检测 FL 训练模型中的变化和异常，以防止中毒攻击。通过在准模拟环境中模拟攻击模式来评估所提出的解决方案。 (c) 2021 Elsevier Ltd. 保留所有权利。
【关键词】联邦学习；区块链；大数据;边缘计算；物联网
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