Abstract
The complete transformation of the supply chain in a truly integrated and fully automated process, presupposes the continuous and endless collection of digital information from every stage of the production scale. The aim is not only to investigate the current situation, but also the history for every stage of the chain. Given the heterogeneity of the systems involved in the supply chain and the non-institutional interoperability in terms of hardware and software, serious objections arise as to how these systems are digitally secured. An important issue is to ensure privacy and business confidentiality. This paper presents a specialized and technologically up-to-date framework for the protection of digital security, privacy and industrial-business secrecy. At its core is Federated Learning technology, which operates over Blockchain and applies advanced encryption techniques.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Bassi, L.: Industry 4.0: Hope, hype or revolution?. In: 2017 IEEE 3rd International Forum on Research and Technologies for Society and Industry (RTSI), 2017, pp. 1–6. https://doi.org/10.1109/RTSI.2017.8065927
Shobol, A., Ali, M.H., Wadi, M., TüR, M.R.: Overview of big data in smart grid. In: 2019 8th International Conference on Renewable Energy Research and Applications (ICRERA), 2019, pp. 1022–1025. https://doi.org/10.1109/ICRERA47325.2019.8996527
Li, C.H., Lau, H.K.: A critical review of product safety in industry 4.0 applications. In: 2017 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), 2017, pp. 1661–1665. https://doi.org/10.1109/IEEM.2017.8290175
Demertzis, K., et al.: Anomaly detection via blockchained deep learning smart contracts in industry 4.0. Neural Comput. Appl. 32(23), 17361–17378 (2020). https://doi.org/10.1007/s00521-020-05189-8
Demertzis, K., Tsiknas, K., Takezis, D., Skianis, C., Iliadis, L.: Darknet traffic big-data analysis and network management for real-time automating of the malicious intent detection process by a weight agnostic neural networks framework. Electronics 10, 781 (2021). https://doi.org/10.3390/electronics10070781
Chen, X., Ji, J., Luo, C., Liao, W., Li, P.: When machine learning meets blockchain: a decentralized, privacy-preserving and secure design. In: 2018 IEEE International Conference on Big Data (Big Data), 2018, pp. 1178–1187. https://doi.org/10.1109/BigData.2018.8622598
Das, D., Sarkar, S.: Machine-to-machine learning based framework for ad-hoc IOT ecosystems. In: 2018 International Conference on Computational Techniques, Electronics and Mechanical Systems (CTEMS), 2018, pp. 431–436. https://doi.org/10.1109/CTEMS.2018.8769148
Tsiknas, K., Taketzis, D., Demertzis, K., Skianis, C.: Cyber threats to industrial IoT: a survey on attacks and countermeasures. IoT 2, 163–186 (2021). https://doi.org/10.3390/iot2010009
Winkel, L.: Real-time ethernet in IEC 61784-2 and IEC 61158 series. In: 2006 4th IEEE International Conference on Industrial Informatics, 2006, pp. 246–250. https://doi.org/10.1109/INDIN.2006.275788
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 IFIP International Federation for Information Processing
About this paper
Cite this paper
Demertzis, K. et al. (2021). Federated Blockchained Supply Chain Management: A CyberSecurity and Privacy Framework. In: Maglogiannis, I., Macintyre, J., Iliadis, L. (eds) Artificial Intelligence Applications and Innovations. AIAI 2021. IFIP Advances in Information and Communication Technology, vol 627. Springer, Cham. https://doi.org/10.1007/978-3-030-79150-6_60
Download citation
DOI: https://doi.org/10.1007/978-3-030-79150-6_60
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-79149-0
Online ISBN: 978-3-030-79150-6
eBook Packages: Computer ScienceComputer Science (R0)
