Secure IoT Data Streams in Cloud-based SAP Systems Using Blockchain and Federated Machine Learning
Keywords:
IoT Security, Blockchain, Federated Machine Learning, SAP Cloud Platform, Data Privacy, Decentralized Learning, Enterprise SystemsAbstract
The integration of Internet of Things (IoT) devices into enterprise environments—particularly in cloud-based SAP (Systems, Applications, and Products) systems—has led to exponential growth in data generation. While this data offers substantial business insights, it also raises critical concerns about data security, privacy, and compliance. This paper proposes a hybrid architecture combining Blockchain and Federated Machine Learning (FML) to secure IoT data streams in cloud-based SAP infrastructures. Blockchain ensures immutability and trust in transactional data, while FML enables decentralized learning across devices without compromising sensitive data. Through architectural modeling, performance analysis, and comparative evaluations, this study illustrates the viability and trade-offs of the proposed solution.
References
Abbas, N., Zhang, Y., Taherkordi, A., & Skeie, T. (2018). Mobile Edge Computing: A Survey. IEEE Internet of Things Journal, 5(1), 450–465.
Uppuluri, V. (2019). The Role of Natural Language Processing (NLP) in Business Intelligence (BI) for Clinical Decision Support. ISCSITR-International Journal of Business Intelligence (ISCSITR-IJBI), 1(2), 1–21. https://doi.org/10.63397/ISCSITR-IJBI_2019_01_02_001
Bonawitz, K., Ivanov, V., Kreuter, B., et al. (2017). Practical secure aggregation for privacy-preserving machine learning. Proceedings of the ACM CCS, 1175–1191.
Christidis, K., & Devetsikiotis, M. (2016). Blockchains and smart contracts for the Internet of Things. IEEE Access, 4, 2292–2303.
Dorri, A., Kanhere, S. S., & Jurdak, R. (2017). Towards an optimized blockchain for IoT. Proceedings of the Second International Conference on Internet-of-Things Design and Implementation, 173–178.
Potla, R.B. (2021). Value-Stream-Aligned Order-to-Cash in SAP S/4HANA: Lead-Time Compression with ATP/BOP and aTO/Pegging Strategies. Journal of Computer Science and Technology Studies, 3(1), 18–26. https://doi.org/10.32996/jcsts
Gubbi, J., Buyya, R., Marusic, S., & Palaniswami, M. (2013). Internet of Things (IoT): A vision, architectural elements, and future directions. Future Generation Computer Systems, 29(7), 1645–1660.
McMahan, H. B., Moore, E., Ramage, D., Hampson, S., & y Arcas, B. A. (2017). Communication-efficient learning of deep networks from decentralized data. Proceedings of AISTATS, 1273–1282.
Roman, R., Najera, P., & Lopez, J. (2013). Securing the Internet of Things. Computer, 44(9), 51–58.
Shokri, R., & Shmatikov, V. (2015). Privacy-preserving deep learning. Proceedings of the 22nd ACM CCS, 1310–1321.
Suriadi, S., Ouyang, C., & ter Hofstede, A. H. M. (2014). Understanding process characteristics: A cross-domain analysis. Information Systems, 46, 264–283.
Zhang, Y., & Wen, J. (2016). The IoT electric business model: Using blockchain technology for the internet of things. Peer-to-Peer Networking and Applications, 10(4), 983–994.
