SIBW: A Swarm Intelligence-Based Network Flow Watermarking Approach for Privacy Leakage Detection in Digital...

Area of Science:

• Digital Health
• Network Security
• Data Privacy

Background:

• Digital healthcare systems face increasing data protection challenges due to exponential growth of sensitive patient information.
• Existing privacy techniques are often inadequate for dynamic, high-loss network environments, hindering effective healthcare data leak detection.
• Frequent medical data breaches compromise the security and reliability of digital healthcare systems.

Purpose of the Study:

• To propose a novel Swarm Intelligence-Based Network Watermarking (SIBW) method for real-time privacy data leakage detection.
• To enhance the robustness and real-time capabilities of privacy protection in digital healthcare networks.

Main Methods:

• Integration of fountain codes with outer error correction codes for robust watermarking.
• Utilization of a Multi-Phase Synergistic Swarm Optimization Algorithm (MPSSOA) for dynamic encoding parameter optimization.
• Implementation of a reliable synchronization sequence and lightweight embedding mechanism for network adaptability.

Main Results:

• SIBW achieves over 90% detection accuracy in high latency jitter and packet loss conditions.
• The method demonstrates superior robustness and efficiency compared to existing techniques.
• SIBW features a compact 3.7 MB design, suitable for resource-constrained systems.

Conclusions:

• The proposed SIBW method provides a robust and efficient solution for real-time privacy data leakage detection in digital healthcare.
• SIBW's adaptability and performance make it ideal for complex and dynamic network environments.
• The compact design facilitates rapid deployment in various digital healthcare settings.