Affiliation:
1. Department of Electrical Engineering and Computer Science, University of Stavanger, 4021 Rogaland, Norway
Abstract
The digitization of healthcare data has presented a pressing need to address privacy concerns within the realm of machine learning for healthcare institutions. One promising solution is federated learning, which enables collaborative training of deep machine learning models among medical institutions by sharing model parameters instead of raw data. This study focuses on enhancing an existing privacy-preserving federated learning algorithm for medical data through the utilization of homomorphic encryption, building upon prior research. In contrast to the previous paper, this work is based upon Wibawa, using a single key for HE, our proposed solution is a practical implementation of a preprint with a proposed encryption scheme (xMK-CKKS) for implementing multi-key homomorphic encryption. For this, our work first involves modifying a simple “ring learning with error” RLWE scheme. We then fork a popular federated learning framework for Python where we integrate our own communication process with protocol buffers before we locate and modify the library’s existing training loop in order to further enhance the security of model updates with the multi-key homomorphic encryption scheme. Our experimental evaluations validate that, despite these modifications, our proposed framework maintains a robust model performance, as demonstrated by consistent metrics including validation accuracy, precision, f1-score, and recall.
Subject
Applied Mathematics,Computational Theory and Mathematics,Computer Networks and Communications,Computer Science Applications,Software
Reference37 articles.
1. Big data security and privacy in healthcare: A Review;Abouelmehdi;Procedia Comput. Sci.,2017
2. Secure, privacy-preserving and federated machine learning in medical imaging;Kaissis;Nat. Mach. Intell.,2020
3. Gilbert, H. (June, January 30). On Ideal Lattices and Learning with Errors over Rings. Proceedings of the Advances in Cryptology—EUROCRYPT 2010, French Riviera, France.
4. Truong, N., Sun, K., Wang, S., Guitton, F., and Guo, Y. (2021). Privacy Preservation in Federated Learning: An insightful survey from the GDPR Perspective. arXiv.
5. PPMA: Privacy-Preserving Multisubset Data Aggregation in Smart Grid;Li;IEEE Trans. Ind. Inform.,2018
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
1. Distributed Learning in the IoT–Edge–Cloud Continuum;Machine Learning and Knowledge Extraction;2024-02-01