The Library
Integration of federated machine learning and blockchain for the provision of secure big data analytics for Internet of Things
Tools
Unal, Devrim, Hammoudeh, Mohammad, Asif Khan, Muhammad, Abuarqoub, Abdelrahman, Epiphaniou, Gregory and Hamila, Ridha (2021) Integration of federated machine learning and blockchain for the provision of secure big data analytics for Internet of Things. Computers & Security, 109 . 102393. doi:10.1016/j.cose.2021.102393 ISSN 0167-4048.
Research output not available from this repository.
Request-a-Copy directly from author or use local Library Get it For Me service.
Official URL: https://doi.org/10.1016/j.cose.2021.102393
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.
Item Type: | Journal Article | ||||||||
---|---|---|---|---|---|---|---|---|---|
Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software | ||||||||
Divisions: | Faculty of Science, Engineering and Medicine > Engineering > WMG (Formerly the Warwick Manufacturing Group) | ||||||||
Journal or Publication Title: | Computers & Security | ||||||||
Publisher: | Elsevier Advanced Technology | ||||||||
ISSN: | 0167-4048 | ||||||||
Official Date: | October 2021 | ||||||||
Dates: |
|
||||||||
Volume: | 109 | ||||||||
Article Number: | 102393 | ||||||||
DOI: | 10.1016/j.cose.2021.102393 | ||||||||
Status: | Peer Reviewed | ||||||||
Publication Status: | Published | ||||||||
Access rights to Published version: | Restricted or Subscription Access |
Request changes or add full text files to a record
Repository staff actions (login required)
View Item |