Secure and robust machine learning for healthcare: A survey

Qayyum, Adnan; Qadir, Junaid; Bilal, Muhammad; Al Fuqaha, Ala

Abstract

Recent years have witnessed widespread adoption of machine learning (ML)/deep learning (DL) techniques due to their superior performance for a variety of healthcare applications ranging from the prediction of cardiac arrest from one-dimensional heart signals to computer-aided diagnosis (CADx) using multi-dimensional medical images. Notwithstanding the impressive performance of ML/DL, there are still lingering doubts regarding the robustness of ML/DL in healthcare settings (which is traditionally considered quite challenging due to the myriad security and privacy issues involved), especially in light of recent results that have shown that ML/DL are vulnerable to adversarial attacks. In this paper, we present an overview of various application areas in healthcare that leverage such techniques from security and privacy point of view and present associated challenges. In addition, we present potential methods to ensure secure and privacy-preserving ML for healthcare applications. Finally, we provide insight into the current research challenges and promising directions for future research.

• Journal Article Type: Article
• Acceptance Date: Jul 31, 2020
• Online Publication Date: Jul 31, 2020
• Publication Date: Jul 31, 2020
• Deposit Date: Sep 2, 2020
• Publicly Available Date: Feb 5, 2021
• Journal: IEEE Reviews in Biomedical Engineering
• Print ISSN: 1937-3333
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Volume: 14
• Pages: 156-180
• DOI: https://doi.org/10.1109/rbme.2020.3013489
• Keywords: Biomedical Engineering
• Public URL: https://uwe-repository.worktribe.com/output/6653747