Enabling Privacy-Assured Fog-Based Data Aggregation in E-Healthcare Systems.

Area of Science:

• Computer Science
• Cybersecurity
• Health Informatics

Background:

• Wearable body area networks are crucial for e-healthcare and telemedicine, especially with the rise of remote monitoring and the COVID-19 pandemic.
• Secure and real-time processing of medical data from these networks is essential for effective healthcare delivery.
• Existing systems face challenges in balancing data security, real-time analysis, and efficient aggregation.

Purpose of the Study:

• To design an improved symmetric homomorphic cryptosystem for secure data analysis.
• To develop a fog-based communication architecture supporting time-sensitive e-healthcare applications.
• To enable secure, real-time analysis of medical data at fog servers for enhanced decision-making.

Main Methods:

• Design of an improved symmetric homomorphic cryptosystem.
• Development of a fog-based communication architecture for e-healthcare data.
• Security evaluation using attack games (e.g., chosen-plaintext attack resilience).
• Performance evaluation and comparison with existing approaches using simulations (Microsoft Azure).

Main Results:

• The proposed approach ensures privacy-assured medical data aggregation.
• The system demonstrates chosen-plaintext attack resilience under the computational Diffie-Hellman assumption.
• Simulation experiments validate the utility and performance of the scheme for real-time analysis.

Conclusions:

• The developed system enhances the security and efficiency of e-healthcare data processing.
• The fog-based architecture facilitates real-time data analysis for improved telemedicine and emergency response.
• This approach offers a robust solution for privacy-assured medical data aggregation in wearable networks.