# Towards Confidential Computing: A Secure Cloud Architecture for Big Data Analytics and AI

arXiv: https://arxiv.org/abs/2305.17761 · HTML: https://mlsanity.com/paper/2305.17761/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 14 references that are in the map.
- First posted on arXiv 28 May 2023.

## Builds on (most cited first, 2 of 2)

- [Distributed Matrix Factorization using Asynchrounous Communication](https://mlsanity.com/paper/1705.10633/) (2017)
- [Enabling EASEY deployment of containerized applications for future HPC systems](https://mlsanity.com/paper/2004.13373/) (2020)

## Abstract (arXiv)

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.