Enhancing Security and Privacy in Healthcare with Generative Artificial Intelligence-Based Detection and Mitigation of Data Poisoning Attacks Software

Authors

  • Yasmin Makki Mohialden Department of Computer Science, College of Science, Mustansiriyah University, Baghdad, Iraq
  • Saba Abdulbaqi Salman Department of Computer Science, College of Education, Al-Iraqia University, Baghdad, Iraq
  • Maad M. Mijwil College of Administration and Economics, Al-Iraqia University, Baghdad, Iraq / Computer Techniques Engineering Department, Baghdad College of Economic Sciences University, Baghdad, Iraq
  • Nadia Mahmood Hussien Department of Computer Science, College of Education, Al-Iraqia University, Baghdad, Iraq
  • Mohammad Aljanabi Imam Ja’afar Al-Sadiq University, Baghdad, Iraq
  • Mostafa Abotaleb Department of System Programming, South Ural State University, Chelyabinsk, Russia
  • Klodian Dhoska Department of Production and Management, Polytechnic University of Tirana, Albania
  • Pradeep Mishra College of Agriculture, Rewa, JNKVV, (M.P.) India

DOI:

https://doi.org/10.35516/jmj.v58i3.2712

Abstract

This study investigated an advanced approach to enhancing security and privacy in healthcare by incorporating artificial intelligence (AI)-based strategies to detect and mitigate data poisoning attacks. The proposed method combined unified learning, homomorphic encryption, and autoencoder-based anomaly detection. It ensured that models were trained in diverse places, protected data, and improved model security. Anomaly identification and mitigation and data poisoning resistance were investigated using simulated medical data. Main results. This approach visualized and assessed model performance. This study offers a complete solution to securing medical data and models against new threats.

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Published

2024-10-15

How to Cite

Mohialden, Y. M. . ., Salman, S. A., Mijwil, M. M. ., Hussien, N. M. ., Aljanabi, M. ., Abotaleb, M. ., Dhoska, K. ., & Mishra, P. . (2024). Enhancing Security and Privacy in Healthcare with Generative Artificial Intelligence-Based Detection and Mitigation of Data Poisoning Attacks Software. Jordan Medical Journal, 58(4). https://doi.org/10.35516/jmj.v58i3.2712

Issue

Vol. 58 No. 4 (2024)

Section

Special Issue