Towards Confidential Computing: A Secure Cloud Architecture for Big Data Analytics and AI
This addresses security concerns for organizations in domains like biomedical research when moving workflows to the cloud, though it appears incremental as it builds on existing cloud and security concepts.
The paper tackles the problem of data security in cloud environments for big data analytics and AI, particularly in sensitive fields like biomedical research, by presenting a secure cloud architecture that ensures data, logic, and computation are protected during transit, use, and at rest.
Cloud computing provisions computer resources at a cost-effective way based on demand. Therefore it has become a viable solution for big data analytics and artificial intelligence which have been widely adopted in various domain science. Data security in certain fields such as biomedical research remains a major concern when moving their workflows to cloud, because cloud environments are generally outsourced which are more exposed to risks. We present a secure cloud architecture and describes how it enables workflow packaging and scheduling while keeping its data, logic and computation secure in transit, in use and at rest.