Differentially-Private Machine Learning for Digital Healthcare
Embedded Files
Research Thrust Summary
The research thrust "Differentially Private Explainable Machine Learning for Digital Healthcare" focuses on developing innovative machine learning techniques that prioritize privacy preservation, transparency, and interpretability in healthcare. It involves designing models that provide accurate predictions while safeguarding sensitive patient data through differential privacy techniques. The project also emphasizes creating explainable machine learning algorithms to enhance trust and understanding in healthcare decision-making. Collaboration with healthcare institutions and real-world data validation aims to advance privacy-aware and transparent machine learning solutions for accurate predictions and informed healthcare choices while upholding patient privacy and regulatory compliance.
Participants
Principle Investigator (PI)
Dr. Abdur Rahman Bin Shahid, Assistant Professor
Collaborators
Dr. Ahmed Imteaj, Assistant Professor, Southern Illinois University Carbondale
Dr. Sajedul Talukder, Assistant Professor, University of Alabama at Birmingham
Melidan Goda, Undergraduate Student, Concord University
Tasnimun Faika, Ford Motors
Publications
Abdur R. Shahid, Sajedul Talukder, and Ahmed Imteaj, "Defending Against Health Attribute Inference Attack in Digital Hand Drawing based Systems Using a Local Differentially Private Machine Learning (ML) Framework." In IEEE Transaction on Artificial Intelligence (In preparation).
Abdur R. Shahid, Sajedul Talukder, and Tasnimun Faika, "Defending Against Personality Prediction Attack in Handwriting Recognition-based Systems Using a Local Differentially Private Machine Learning (ML) Framework". Book Chapter in Artificial Intelligence in Cybersecurity: The State of the art, IOS-Press, USA, 2023. [Chapter proposal accepted]
Abdur R. Shahid, Sajedul Talukder, "A Study of Differentially Private Machine Learning in Healthcare". In 2021 Innovations in Intelligent Systems and Applications Conference (ASYU). IEEE, 2021.
Abdur R. Shahid, Sajedul Talukder, "Evaluating Machine Learning Models for Handwriting Recognition-based Systems under Local Differential Privacy". In 2021 Innovations in Intelligent Systems and Applications Conference (ASYU). IEEE, 2021.
Abdur R. Shahid, and Sajedul Talukder, "Evaluating Machine Learning Models for Handwriting Recognition-based Systems under Local Differential Privacy", 2021 Innovations in Intelligent Systems and Applications Conference (ASYU), pp. 1-6. IEEE, 2021
Abdur R. Shahid, and Sajedul Talukder, "A Study of Differentially Private Machine Learning in Healthcare", 2021 Innovations in Intelligent Systems and Applications Conference (ASYU), pp. 1-6. IEEE, 2021
Abdur R. Shahid, and Sajedul Talukder, "Evaluation of Privacy-Preserving Logistic Regression and Naive Bayes Classifiers in Healthcare", In Proceedings of the 37th ACM CCSC Eastern Conference (ACM CCSC), October 2021.
Abdur R. Shahid, and Sajedul Talukder, "Applying Local Differential Privacy in Handwriting Recognition-based Systems", In proceedings of the 37th ACM CCSC Eastern Conference (ACM CCSC), October 2021.
Melinda Goda, Abdur R. Shahid, "Differentially Private Machine Learning for Breast Cancer Classification", Consortium for Computing Sciences in Colleges — Northeastern Region, 2021 (Runner Up in Best Undergraduate Poster Award)
Page updated
Google Sites
Report abuse