Secure and Robust Machine Learning for Healthcare: A Survey
By Adnan Qayyum, Junaid Qadir, Muhammad Bilal, Ala Al-Fuqaha. IEEE Reviews in Biomedical Engineering, 2020
471 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence, Radiology, Nuclear Medicine and Imaging
View Paper: https://www.semanticscholar.org/paper/e5e41ca6d5ebbf5f8ef1c77791fa4c75ef1ceb1b
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Yuji Roh
- Ala Al-Fuqaha
- Junaid Qadir
- Steven Euijong Whang
- Khansa Rasheed
- Mohamed Ghaly
- Adeel Razi
- Jae-Gil Lee
In Radiology, Nuclear Medicine and Imaging
- Ala Al-Fuqaha
- Junaid Qadir
- Adeel Razi
- Adnan Qayyum
- Shakked Noy
- Whitney Zhang
- Louisa H.Y. Tsi
- Cecilia Ka Yuk Chan
Citations in the corpus
- Explainable, trustworthy, and ethical machine learning for healthcare: A survey (2021, background): “Privacy attacks on data integrity can be of two types, i.e., learning about the confidential information and malicious use of data [32].”
- Benchmarking Differentially Private Residual Networks for Medical Imagery (2020, unknown): “Other works relevant to general privacy preserving techniques for health records include (Kim et al., 2018; Kaissis et al., 2020; Adam et al., 2007; Qayyum et al., 2020; Dankar & El Emam, 2013; Hossein et al., 2019; Faravelon & Verdier, 2010).”
- Secure, privacy-preserving and federated machine learning in medical imaging (2020, background): “However, the issues of security and privacy are not limited to medical imaging 82 , as seen for example in the 2019/2020 SARS-CoV2-pandemic, which sparked worldwide concern about the implications of setting political, ethical and legal precedents by large-scale automatic contact tracing and…”
- Data collection and quality challenges in deep learning: a data-centric AI perspective (2021, background): “For example, privacy machine learning aims to respect the privacy of the users providing the data [124].”