论文标题
使用容器和负载平衡服务在云中确保您的协作jupyter笔记本
Securing Your Collaborative Jupyter Notebooks in the Cloud using Container and Load Balancing Services
论文作者
论文摘要
Jupyter已成为开发数据应用程序的首选平台，但是数据和安全问题，尤其是在处理医疗保健时，对于许多机构和处理敏感信息的应用程序而言，已成为最重要的。那么，我们如何继续享受Jupyter和Python生态系统提供的数据分析和机器学习机会，同时保证可审核的遵守安全和隐私问题？我们将根据Jupyter进行基于云平台的架构和实现，该平台基于Jupyter，该平台与Amazon Web Services（AWS）集成并使用集装服务，而无需将平台曝光到Kubernetes和Jupyterhub中存在的漏洞。该体系结构解决了HIPAA的要求，以确保数据的安全性和隐私。该体系结构使用AWS服务来提供JSON Web令牌（JWT）进行身份验证和网络控制。此外，我们的体系结构可以安全的协作和共享Jupyter笔记本电脑。即使我们的平台专注于Jupyter笔记本电脑和Jupyterlab，它也支持共享相同身份验证机制的R-Studio和定制应用程序。此外，该平台可以扩展到AWS以外的其他云服务。
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.
