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

Area of Science:

• Artificial Intelligence
• Healthcare Informatics
• Cybersecurity

Background:

• Deep learning in e-healthcare offers diagnostic and treatment benefits but introduces significant patient data privacy risks.
• Ensuring patient data security is paramount in e-healthcare applications.

Purpose of the Study:

• To propose a novel approach combining deep learning and multi-agent systems to enhance privacy in e-healthcare.
• To implement multi-agent-based privacy metrics for real-time monitoring and control of patient data access.

Main Methods:

• A multi-agent system was developed to manage data access, assigning roles and permissions to agents.
• Privacy metrics (confidentiality, integrity, availability) were employed for real-time evaluation of data access.
• A deep learning model was integrated for improved diagnostic and treatment plan accuracy.

Main Results:

• The system ensures only authorized agents access patient data, significantly enhancing e-healthcare security.
• Real-time privacy metric evaluation effectively identifies potential privacy violations, reducing breach risks.
• The integrated deep learning model demonstrated improved accuracy in diagnoses and treatment planning.

Conclusions:

• The multi-agent-based privacy metrics approach provides a robust framework for secure e-healthcare deep learning.
• This integrated system balances advanced AI capabilities with essential patient data privacy.
• The approach leads to improved patient outcomes through enhanced security and diagnostic precision.