Methods and protocols that preserve privacy while allowing data analysis and exchange are collectively called privacy-preserving strategies. Regarding patient information, privacy-preserving methods guarantee its safe handling, sharing, and analysis without compromising the patient's privacy. For patient privacy and regulatory compliance, healthcare apps must use privacy-protecting techniques.
Published in Chapter:
Secure and Privacy-Preserving Federated Learning With Explainable Artificial Intelligence for Smart Healthcare Systems
Copyright: © 2024
|Pages: 26
DOI: 10.4018/979-8-3693-1874-4.ch018
Abstract
With the escalating global population, the healthcare sector faces unprecedented challenges, necessitating innovative solutions. Deep learning (DL) and federated learning (FL) have emerged as pivotal technologies, yet challenges persist in data privacy, security, and model interpretability, especially in healthcare applications. This research addresses these challenges by proposing robust frameworks for secure, privacy-preserving federated learning with explainable artificial intelligence in smart healthcare systems. The objective is to enhance the security, performance, and privacy of healthcare systems, ensuring their resilience and effectiveness in real-world scenarios. The research employs a literature approach. This comprehensive approach establishes a foundation for the future development of smart healthcare systems, fostering trust, transparency, and efficiency in healthcare decision-making processes.