COVID-19 Imaging Data Privacy by Federated Learning Design: A Theoretical Framework
This addresses privacy concerns for healthcare providers and researchers handling sensitive COVID-19 data, but it appears incremental as it combines existing concepts of differential privacy and federated learning.
The paper tackles the problem of ensuring data privacy in COVID-19 imaging data sharing for disease diagnosis by proposing a differential privacy by design (dPbD) framework integrated into federated learning systems, aiming to enhance privacy with scalability and robustness.
To address COVID-19 healthcare challenges, we need frequent sharing of health data, knowledge and resources at a global scale. However, in this digital age, data privacy is a big concern that requires the secure embedding of privacy assurance into the design of all technological solutions that use health data. In this paper, we introduce differential privacy by design (dPbD) framework and discuss its embedding into the federated machine learning system. To limit the scope of our paper, we focus on the problem scenario of COVID-19 imaging data privacy for disease diagnosis by computer vision and deep learning approaches. We discuss the evaluation of the proposed design of federated machine learning systems and discuss how differential privacy by design (dPbD) framework can enhance data privacy in federated learning systems with scalability and robustness. We argue that scalable differentially private federated learning design is a promising solution for building a secure, private and collaborative machine learning model such as required to combat COVID19 challenge.