Privacy-aware Early Detection of COVID-19 through Adversarial Training

• Main Authors: Rohanian, Omid, Kouchaki, Samaneh, Soltan, Andrew, Yang, Jenny, Rohanian, Morteza, Yang, Yang, Clifton, David
• Format: Journal Article
• Language: English
• Published: 09-01-2022
• Subjects: Computer Science - Artificial Intelligence; Computer Science - Cryptography and Security; Computer Science - Learning
• DOI: 10.48550/arxiv.2201.03004

Early detection of COVID-19 is an ongoing area of research that can help with
triage, monitoring and general health assessment of potential patients and may
reduce operational strain on hospitals that cope with the coronavirus pandemic.
Different machine learning techniques have been used in the literature to
detect coronavirus using routine clinical data (blood tests, and vital signs).
Data breaches and information leakage when using these models can bring
reputational damage and cause legal issues for hospitals. In spite of this,
protecting healthcare models against leakage of potentially sensitive
information is an understudied research area. In this work, we examine two
machine learning approaches, intended to predict a patient's COVID-19 status
using routinely collected and readily available clinical data. We employ
adversarial training to explore robust deep learning architectures that protect
attributes related to demographic information about the patients. The two
models we examine in this work are intended to preserve sensitive information
against adversarial attacks and information leakage. In a series of experiments
using datasets from the Oxford University Hospitals, Bedfordshire Hospitals NHS
Foundation Trust, University Hospitals Birmingham NHS Foundation Trust, and
Portsmouth Hospitals University NHS Trust we train and test two neural networks
that predict PCR test results using information from basic laboratory blood
tests, and vital signs performed on a patients' arrival to hospital. We assess
the level of privacy each one of the models can provide and show the efficacy
and robustness of our proposed architectures against a comparable baseline. One
of our main contributions is that we specifically target the development of
effective COVID-19 detection models with built-in mechanisms in order to
selectively protect sensitive attributes against adversarial attacks.