Security Model for the privacy of Big Data in Health Care Cloud using Fog Computing
DOI:
https://doi.org/10.21070/pels.v8i2.2566Keywords:
Health Care Cloud Security, Big Data Privacy, Fog Computing, Encryption in Healthcare, Anomaly Detection in Cloud Systems.Abstract
General Background: The rapid integration of big data within health care cloud systems has transformed clinical data management but also intensified concerns regarding security and patient privacy. Specific Background: Existing cloud-based health infrastructures struggle to ensure confidentiality, integrity, and low-latency access, particularly as data volumes grow and computational demands increase. Knowledge Gap: Current models emphasize encryption or access control in isolation and provide limited solutions for latency, localized processing, and multi-layer threat detection, with minimal exploration of fog computing as an integrated security enhancer. Aims: This study proposes a comprehensive security framework that strengthens privacy protection for big data in health care cloud environments through combined encryption, role-based access control, anomaly detection, and fog computing integration. Results: Simulation-based evaluation demonstrates notable improvements, including reduced latency, enhanced data privacy, and high fog-node efficiency, indicating effective real-time processing and minimized exposure of sensitive data. Novelty: The model introduces a multilayered security architecture that strategically incorporates fog nodes to enable localized analysis, secure key management, and dynamic threat detection, offering a holistic approach absent from prior studies. Implications: The findings highlight a scalable and efficient security paradigm capable of improving resilience, privacy preservation, and operational performance in modern cloud-based health care systems.
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The model integrates encryption, RBAC, and anomaly detection to strengthen data confidentiality in cloud-based healthcare systems.
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Fog computing reduces latency and limits data exposure by enabling localized processing near data sources.
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Simulation results show notable gains in latency reduction, data privacy, and fog node efficiency.
Keywords : Health Care Cloud Security, Big Data Privacy, Fog Computing, Encryption in Healthcare, Anomaly Detection in Cloud Systems.
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