  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.
