COVID-19 Imaging Data Privacy by Federated Learning Design: A Theoretical Framework

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.

[1]  Manoranjan Paul,et al.  Potential Features of ICU Admission in X-ray Images of COVID-19 Patients , 2020, ArXiv.

[2]  Manoranjan Paul,et al.  COVID-19 Detection Through Transfer Learning Using Multimodal Imaging Data , 2020, IEEE Access.

[3]  Manoranjan Paul,et al.  COVID-19 Control by Computer Vision Approaches: A Survey , 2020, IEEE Access.

[4]  H. Vincent Poor,et al.  Federated Learning With Differential Privacy: Algorithms and Performance Analysis , 2019, IEEE Transactions on Information Forensics and Security.

[5]  Urs Gasser,et al.  Digital tools against COVID-19: taxonomy, ethical challenges, and navigation aid , 2020, The Lancet Digital Health.

[6]  Payman Mohassel,et al.  SecureML: A System for Scalable Privacy-Preserving Machine Learning , 2017, 2017 IEEE Symposium on Security and Privacy (SP).

[7]  Boi Faltings,et al.  Federated Learning with Bayesian Differential Privacy , 2019, 2019 IEEE International Conference on Big Data (Big Data).

[8]  Noorbakhsh Amiri Golilarz,et al.  Blockchain-Federated-Learning and Deep Learning Models for COVID-19 Detection Using CT Imaging , 2020, IEEE Sensors Journal.

[9]  Tassilo Klein,et al.  Differentially Private Federated Learning: A Client Level Perspective , 2017, ArXiv.

[10]  Joanna Bryson,et al.  Modularity and Design in Reactive Intelligence , 2001, IJCAI.

[11]  Charles Elkan,et al.  Differential Privacy and Machine Learning: a Survey and Review , 2014, ArXiv.

[12]  Peter Schaar,et al.  Privacy by Design , 2010 .

[13]  Tianjian Chen,et al.  Federated Machine Learning: Concept and Applications , 2019 .

[14]  Paul Voigt,et al.  The EU General Data Protection Regulation (GDPR) , 2017 .

[15]  Paul Voigt,et al.  The Eu General Data Protection Regulation (Gdpr): A Practical Guide , 2017 .

[16]  K. Schulman,et al.  Covid-19 and Health Care's Digital Revolution. , 2020, The New England journal of medicine.

[17]  Wim Naudé,et al.  Artificial intelligence vs COVID-19: limitations, constraints and pitfalls , 2020, AI & SOCIETY.

[18]  Yong Li,et al.  A Survey on Edge Intelligence , 2020, ArXiv.

[19]  Ian Goodfellow,et al.  Deep Learning with Differential Privacy , 2016, CCS.

[20]  Manoranjan Paul,et al.  Computer Vision for COVID-19 Control: A Survey , 2020, ArXiv.

[21]  I. Rubinstein.  Regulating Privacy by Design , 2011 .

[22]  Anwaar-ul-Haq,et al.  An optimized image fusion algorithm for night-time surveillance and navigation , 2005, Proceedings of the IEEE Symposium on Emerging Technologies, 2005..

[23]  Jakub Konecný,et al.  Federated Optimization: Distributed Optimization Beyond the Datacenter , 2015, ArXiv.

[24]  Anit Kumar Sahu,et al.  Federated Learning: Challenges, Methods, and Future Directions , 2019, IEEE Signal Processing Magazine.

[25]  Ali Makhdoumi,et al.  Privacy-utility tradeoff under statistical uncertainty , 2013, 2013 51st Annual Allerton Conference on Communication, Control, and Computing (Allerton).

[26]  Marcello Ienca,et al.  On the responsible use of digital data to tackle the COVID-19 pandemic , 2020, Nature Medicine.

[27]  Prateek Mittal,et al.  Analyzing Federated Learning through an Adversarial Lens , 2018, ICML.

[28]  Zaïd Harchaoui,et al.  Robust Aggregation for Federated Learning , 2019, IEEE Transactions on Signal Processing.

[29]  Yong Li,et al.  Edge Intelligence: Architectures, Challenges, and Applications , 2020 .

[30]  Stuart S. Shapiro,et al.  Privacy by design , 2010, Commun. ACM.

[31]  Yifan Yang,et al.  Experiments of Federated Learning for COVID-19 Chest X-ray Images , 2020, Advances in Artificial Intelligence and Security.

[32]  Peter Richtárik,et al.  Federated Optimization: Distributed Machine Learning for On-Device Intelligence , 2016, ArXiv.

[33]  Cynthia Dwork,et al.  Differential Privacy: A Survey of Results , 2008, TAMC.