2020/06/02 by Haw-minn Lu, Lu, Haw-minn, Adrian Kwong +4 · 1 citation
Computer Science · Decision Sciences · #Cryptography and Security (cs.CR) #Data Quality and Management #FOS: Computer and information sciences #Networking and Internet Architecture (cs.NI) #Scientific Computing and Data Management #cs.CR #cs.NI
paper · pdf · doi:10.48550/arxiv.2006.01818
Accepted and submitted to 19th Python in Science Conference. (SciPy 2020)
arxiv created 2020/06/02 · openalex publication_date 2020/06/02 · arxiv updated 2020/06/03 · openalex created_date 2020/06/12 · openalex updated_date 2026/07/28
Jupyter has become the go-to platform for developing data applications but data and security concerns, especially when dealing with healthcare, have become paramount for many institutions and applications dealing with sensitive information. How then can we continue to enjoy the data analysis and machine learning opportunities provided by Jupyter and the Python ecosystem while guaranteeing auditable compliance with security and privacy concerns? We will describe the architecture and implementation of a cloud based platform based on Jupyter that integrates with Amazon Web Services (AWS) and uses containerized services without exposing the platform to the vulnerabilities present in Kubernetes and JupyterHub. This architecture addresses the HIPAA requirements to ensure both security and privacy of data. The architecture uses an AWS service to provide JSON Web Tokens (JWT) for authentication as well as network control. Furthermore, our architecture enables secure collaboration and sharing of Jupyter notebooks. Even though our platform is focused on Jupyter notebooks and JupyterLab, it also supports R-Studio and bespoke applications that share the same authentication mechanisms. Further, the platform can be extended to other cloud services other than AWS.