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

• Published in: IEEE journal of biomedical and health informatics Vol. PP; pp. 1 - 12
• Main Authors: Qiao, Sibo, Guo, Qiang, Shi, Fengdong, Wang, Min, Zhu, Haohao, Khan, Fazlullah, Rodrigues, Joel J. P. C., Lyu, Zhihan
• Format: Journal Article
• Language: English
• Published: United States IEEE 14.02.2025
• Subjects: Data privacy; Digital healthcare systems; Electronic healthcare; Electronic mail; Encoding; Heuristic algorithms; Network flow watermarking; Optimization; Particle swarm optimization; Privacy data leakage detection; Real-time systems; Robustness; Swarm intelligence optimization; Watermarking
• ISSN: 2168-2194; 2168-2208; 2168-2208
• DOI: 10.1109/JBHI.2025.3542561

The exponential growth of sensitive patient information and diagnostic records in digital healthcare systems has increased the complexity of data protection, while frequent medical data breaches severely compromise system security and reliability. Existing privacy protection techniques often lack robustness and real-time capabilities in high-noise, high-packet-loss, and dynamic network environments, limiting their effectiveness in detecting healthcare data leaks. To address these challenges, we propose a Swarm Intelligence-Based Network Watermarking (SIBW) method for real-time privacy data leakage detection in digital healthcare systems. SIBW integrates fountain codes with outer error correction codes and employs a Multi-Phase Synergistic Swarm Optimization Algorithm (MPSSOA) to dynamically optimize encoding parameters, significantly enhancing the robustness and interference resistance of watermark detection. Additionally, a reliable synchronization sequence and lightweight embedding mechanism are designed to ensure adaptability to complex, dynamic networks. Experimental results demonstrate that SIBW achieves over 90% detection accuracy under high latency jitter and packet loss conditions, surpassing existing methods in both robustness and efficiency. With a compact design of only 3.7 MB, SIBW is particularly suited for rapid deployment in resource-constrained digital healthcare systems.