Advancing security and privacy measures in telehealth IoT/Fog/Cloud ecosystems
Bibliographic record
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".