Vol. 4 No. 1 (2024): January−June 2024
Articles

A Privacy-Preserving Federated Learning Framework for Healthcare Big Data Analytics in Multi-Cloud Environments

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  • Hanqing Zhang,
  • Enmiao Feng,
  • Haisheng Lian
Hanqing Zhang
Information Studies, Trine University, AZ, USA
Enmiao Feng
Electrical & Computer Engineering, Duke University, NC, USA
Haisheng Lian
Material Physics, Sun Yat-sen University, Guangzhou, China

Published 2024-05-16

Keywords

  • Federated Privacy Preservation Learning,
  • Big Health Data Analysis,
  • Multi-Nuns Environments,
  • Homomorphic Cryptography

How to Cite

Zhang, H., Feng, E., & Lian, H. (2024). A Privacy-Preserving Federated Learning Framework for Healthcare Big Data Analytics in Multi-Cloud Environments. Spectrum of Research, 4(1). Retrieved from https://spectrumofresearch.com/index.php/sr/article/view/14

Copyright (c) 2024 Spectrum of Research

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

This paper presents SecureHealth, a comprehensive privacy-preserving federated learning framework designed specifically for healthcare big data analytics in multi-cloud environments. The framework addresses the critical challenge of enabling collaborative learning across healthcare institutions without compromising patient privacy or regulatory compliance. SecureHealth implements a multi-layered privacy protection approach integrating homomorphic encryption based on the RNS-CKKS scheme, differential privacy with adaptive budget allocation, and secure multi-party computation with threshold-based protocols. The proposed architecture supports distributed model training across heterogeneous healthcare datasets located in different cloud environments while eliminating raw data sharing. Experimental evaluation on real-world healthcare datasets demonstrates that SecureHealth achieves model accuracy comparable to centralized approaches (within 3-7%) while providing strong privacy guarantees. Performance analysis confirms computational efficiency with acceptable overhead (10-40x depending on model complexity) and optimized communication requirements across cloud environments. Security analysis establishes theoretical privacy guarantees and empirical resistance against various inference attacks. The structure offers perfect integration with existing health IT infrastructure on major cloud platforms, maintaining regulatory compliance. Securehealth allows health institutions to participate in collaborative learning, maintaining full control over sensitive patients of patients, promoting privacy preservation analysis for better medical care results.

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