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المؤلفUnal, Devrim
المؤلفHammoudeh, Mohammad
المؤلفKhan, Muhammad Asif
المؤلفAbuarqoub, Abdelrahman
المؤلفEpiphaniou, Gregory
المؤلفHamila, Ridha
تاريخ الإتاحة2023-04-04T09:09:12Z
تاريخ النشر2021
اسم المنشورComputers and Security
المصدرScopus
معرّف المصادر الموحدhttp://dx.doi.org/10.1016/j.cose.2021.102393
معرّف المصادر الموحدhttp://hdl.handle.net/10576/41668
الملخصBig 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. 2021
راعي المشروعThis publication was made possible by NPRP grant NPRP 10-0125-170250 from the Qatar National Research Fund (a member of Qatar Foundation).
اللغةen
الناشرElsevier
الموضوعBig data
Blockchain
Edge computing
Federated learning
IoT
العنوانIntegration of federated machine learning and blockchain for the provision of secure big data analytics for Internet of Things
النوعArticle
رقم المجلد109
dc.accessType Abstract Only


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