Securing AI-Enhanced Health Data Management Systems Against Adversarial Attacks Over 6G Networks

About the Project

The project will focus on addressing the crucial need for security in the ongoing field of AI-enhanced healthcare systems, particularly those operating over next-generation 6G networks. With the integration of AI into healthcare, systems become vulnerable to adversarial attacks that can manipulate or mislead AI decision-making processes. The research will fortify AI systems against such attacks within a decentralized health data management context. The selected candidate is expected to achieve following objectives:

• Theoretical Analysis of Adversarial Threats: Study and classify the types of adversarial attacks pertinent to AI in healthcare, including those targeting data integrity and AI model behaviour. Analyse vulnerabilities specific to decentralized systems and 6G network architectures.
• Development of AI Robustness Techniques: Innovate and test new AI algorithms designed to detect and resist adversarial inputs, integrating these algorithms into the health data management framework. Then employ machine learning / deep learning techniques to automatically adapt and respond to new threats.
• Blockchain Integration for Enhanced Security: Utilize blockchain technology to create an immutable record of data transactions, providing a foundational layer of security and traceability. Explore blockchain's role in securely managing access and identity verification across distributed networks.
• 6G Network-Specific Security Enhancements: Investigate the unique capabilities of 6G networks to support ultra-secure and low-latency data transmissions. Design network protocols that enhance data privacy and AI system security in high-speed mobile environments.
• Simulation and Testing: Develop a simulated 6G network environment to test the resilience of the AI systems against adversarial attacks. Use real-world health data and scenarios to evaluate the effectiveness of the proposed defences.
• Ethical and Regulatory Compliance: Conduct a comprehensive review of ethical issues related to AI in healthcare, focusing on patient consent, data privacy, and the implications of AI decision-making. Ensure that all proposed solutions comply with international health standards and data protection regulations.

Funding Notes

there is no funding for this project

References

• Usman MR, Usman MA, Shin SY. A novel encoding-decoding scheme using Huffman coding for multimedia networks. In2018 15th IEEE Annual Consumer Communications & Networking Conference (CCNC) 2018 Jan 12 (pp. 1-6). IEEE.
• Jang YS, Usman MR, Usman MA, Shin SY. Swapped Huffman tree coding application for low-power wide-area network (LPWAN). In2016 international conference on smart green technology in electrical and information systems (ICSGTEIS) 2016 Oct 6 (pp. 53-58). IEEE.
• Usman MA, Usman MR. Using image steganography for providing enhanced medical data security. In2018 15th IEEE Annual Consumer Communications & Networking Conference (CCNC) 2018 Jan 12 (pp. 1-4). IEEE.
• Usman MA, Usman MR, Shin SY. An intrusion oriented heuristic for efficient resource management in end-to-end wireless video surveillance systems. In2018 15th IEEE Annual Consumer Communications & Networking Conference (CCNC) 2018 Jan 12 (pp. 1-6). IEEE.
• Usman MA, Usman MR, Satrya GB, Muhammad Ashfaq K, Politis C, Philip N, Shin SY. QI-BRiCE: Quality index for bleeding regions in capsule endoscopy videos. Computers, Materials & Continua. 2021;67(2):1697-712.
• Usman MA, Usman MR, Shin SY. Quality assessment for wireless capsule endoscopy videos compressed via HEVC: From diagnostic quality to visual perception. Computers in biology and medicine. 2017 Dec 1;91:112-34.