A nomadic multi-agent based privacy metrics for e-health care: a deep learning approach

• Published in: Multimedia tools and applications Vol. 83; no. 3; pp. 7249 - 7272
• Main Authors: Dhasarathan, Chandramohan, Shanmugam, M., Kumar, Manish, Tripathi, Diwakar, Khapre, Shailesh, Shankar, Achyut
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.01.2024; Springer Nature B.V
• Subjects: Access control; Computer Communication Networks; Computer Science; Data Structures and Information Theory; Deep learning; Diagnosis; Health care; Health services; Medical diagnosis; Medical electronics; Multiagent systems; Multimedia Information Systems; Patients; Privacy; Reagents; Real time; Special Purpose and Application-Based Systems; Security; Knowledgebase; Privacy Preserving; Deep learning system; Multi-Agent
• ISSN: 1380-7501; 1573-7721
• DOI: 10.1007/s11042-023-15363-4

In recent years, there has been a surge in the use of deep learning systems for e-healthcare applications. While these systems can provide significant benefits regarding improved diagnosis and treatment, they also pose substantial privacy risks to patients' sensitive data. Privacy is a crucial issue in e-healthcare, and it is essential to keep patient information secure. A new approach based on multi-agent-based privacy metrics for e-healthcare deep learning systems has been proposed to address this issue. This approach uses a combination of deep learning and multi-agent systems to provide a more robust and secure method for e-healthcare applications. The multi-agent system is designed to monitor and control the access to patients' data by different agents in the system. Each agent is assigned a specific role and has specific data access permissions. The system employs a set of privacy metrics to a substantial privacy level of the data accessed by each agent. These metrics include confidentiality, integrity, and availability, evaluated in real-time and used to identify potential privacy violations. In addition to the multi-agent system, the deep learning component is also integrated into the system to improve the accuracy of diagnoses and treatment plans. The deep learning model is trained on a large dataset of medical records and can accurately predict the diagnosis and treatment plan based on the patient's symptoms and medical history. The multi-agent-based privacy metrics for the e-healthcare deep learning system approach have several advantages. It provides a more secure system for e-healthcare applications by ensuring only authorized agents can access patients' data. Privacy metrics enable the system to identify potential privacy violations in real-time, thereby reducing the risk of data breaches. Finally, integrating deep learning improves the accuracy of diagnoses and treatment plans, leading to better patient outcomes.