An advanced data fabric architecture leveraging homomorphic encryption and federated learning
<|reference_start|>An advanced data fabric architecture leveraging homomorphic encryption and federated learning: Data fabric is an automated and AI-driven data fusion approach to accomplish data management unification without moving data to a centralized location for solving complex data problems. In a Federated learning architecture, the global model is trained based on the learned parameters of several local models that eliminate the necessity of moving data to a centralized repository for machine learning. This paper introduces a secure approach for medical image analysis using federated learning and partially homomorphic encryption within a distributed data fabric architecture. With this method, multiple parties can collaborate in training a machine-learning model without exchanging raw data but using the learned or fused features. The approach complies with laws and regulations such as HIPAA and GDPR, ensuring the privacy and security of the data. The study demonstrates the method's effectiveness through a case study on pituitary tumor classification, achieving a significant level of accuracy. However, the primary focus of the study is on the development and evaluation of federated learning and partially homomorphic encryption as tools for secure medical image analysis. The results highlight the potential of these techniques to be applied to other privacy-sensitive domains and contribute to the growing body of research on secure and privacy-preserving machine learning.<|reference_end|>
@article{rieyan2024an,
    title={An advanced data fabric architecture leveraging homomorphic encryption
  and federated learning},
    author={Sakib Anwar Rieyan, Md. Raisul Kabir News, A.B.M. Muntasir Rahman,
  Sadia Afrin Khan, Sultan Tasneem Jawad Zaarif, Md. Golam Rabiul Alam,
  Mohammad Mehedi Hassan, Michele Ianni, Giancarlo Fortino},
    journal={Information Fusion, 102, 102004 (2024)},
    year={2024},
    doi={10.1016/j.inffus.2023.102004},
    archivePrefix={arXiv},
    eprint={2402.09795},
    primaryClass={cs.CR cs.AI cs.DB}
}