COVID-19 Imaging Data Privacy by Federated Learning Design: A Theoretical Framework
arXiv:2010.06177
Abstract
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.
2 images, 0 Table,8 pages
References in corpus (9)
- Deep Learning with Differential Privacy
- Federated Optimization: Distributed Machine Learning for On-Device Intelligence
- Robust Aggregation for Federated Learning
- Federated Optimization:Distributed Optimization Beyond the Datacenter
- Differentially Private Federated Learning: A Client Level Perspective
- Differential Privacy and Machine Learning: a Survey and Review
- Edge Intelligence: Architectures, Challenges, and Applications
- Blockchain-Federated-Learning and Deep Learning Models for COVID-19 detection using CT Imaging
- Experiments of Federated Learning for COVID-19 Chest X-ray Images