Advancing security and privacy measures in telehealth IoT/Fog/Cloud ecosystems

Abstract

Background: As Telehealth becomes integral to modern healthcare, ensuring the security and privacy of patient data in remote monitoring scenarios is paramount. This paper presents an advanced security and privacy model designed to safeguard Telehealth systems, addressing the evolving threats in the interconnected landscape of IoT, Fog, and Cloud. Purpose: The purpose of this research is to evaluate the effectiveness of the proposed security and privacy model in real-world Telehealth scenarios through a comprehensive simulation study. The model integrates encryption, key management, intrusion detection, and privacy- preserving measures to establish end-to-end protection for patient data. Methods: A simulation study is conducted, focusing on many distinct threat scenarios: unauthorized access, physical security breaches at Fog nodes, and cloud server data breaches, etc. Each scenario involves a detailed setup of the Telehealth ecosystem, simulation of threats, and assessment of the security model's components. Key metrics, including detection rates, response times, and mitigation effectiveness, are recorded. Results: The simulation results reveal the model's success in detecting and responding to unauthorized access attempts and cloud server breaches, with notable strengths in encryption and intrusion detection systems. However, challenges are identified in physical security measures and the prevention of insider threats, indicating areas for refinement. Conclusion: In conclusion, the proposed security and privacy model demonstrates efficacy in securing patient data across Telehealth IoT/Fog/Cloud systems. The results underscore the dynamic nature of security challenges, emphasizing the need for continuous refinement. The model provides a foundation for adaptive security frameworks, ensuring resilience against emerging threats in the evolving landscape of healthcare technology.