论文标题
COVID-19通过联合学习设计成像数据隐私：一个理论框架
COVID-19 Imaging Data Privacy by Federated Learning Design: A Theoretical Framework
论文作者
论文摘要
为了应对COVID-19-19医疗保健挑战，我们需要在全球范围内经常共享健康数据，知识和资源。但是，在这个数字时代，数据隐私是一个很大的问题，它需要将隐私保证的安全嵌入到使用健康数据的所有技术解决方案的设计中。在本文中，我们通过设计（DPBD）框架介绍了差异隐私，并讨论了其嵌入到联合机器学习系统中。为了限制论文的范围，我们专注于通过计算机视觉和深度学习方法诊断的COVID-19成像数据隐私的问题情景。我们讨论了对联合机器学习系统的拟议设计的评估，并讨论了设计（DPBD）框架的差异隐私如何通过可扩展性和鲁棒性增强联合学习系统中的数据隐私。我们认为，可扩展的私人联合学习设计是建立安全，私人和协作的机器学习模型的有前途的解决方案，例如打击Covid19挑战所需。
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.
