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.
Anwaar Ulhaq, Oliver Burmeister