Hardware Isolation Approach to Securely Use Untrusted GPUS in Cloud Environments for Machine Learning

Author

Lucas D. Hall, The University of Texas Rio Grande Valley

Date of Award

5-2022

Document Type

Thesis

Degree Name

Master of Science (MS)

Department

Computer Science

First Advisor

Dr. Lei Xu

Second Advisor

Dr. Sheikh Ariful Islam

Third Advisor

Dr. Honglu Jiang

Abstract

Machine Learning (ML) is now a primary method for getting useful information out of the immense volumes of data being generated and stored in society today. Useful data is a commodity for training ML models and those that need data for training are often not the owners of the data leading to a desire to use cloud-based services. Deep learning algorithms are best suited to run on a graphical processing unit (GPU) which presents a specific problem since the GPU is not a secure or trusted piece of hardware in the cloud computing environment.

In this paper, we will analyze some current methods of performing ML in the cloud using untrusted hardware and propose FIGHTE: full isolation of GPU hardware for trusted execution, a new hardware implementation capable of physical isolation. FIGHTE should allow for securely using a GPU for ML in the cloud even for various parties involved.

Comments

Copyright 2022 Lucas D. Hall. All Rights Reserved.

https://go.openathens.net/redirector/utrgv.edu?url=https://www.proquest.com/dissertations-theses/hardware-isolation-approach-securely-use/docview/2699720599/se-2?accountid=7119

Recommended Citation

Hall, Lucas D., "Hardware Isolation Approach to Securely Use Untrusted GPUS in Cloud Environments for Machine Learning" (2022). Theses and Dissertations. 1051.
https://scholarworks.utrgv.edu/etd/1051