Theses and Dissertations

Date of Award


Document Type


Degree Name

Master of Science (MS)


Computer Science

First Advisor

Dr. Lei Xu

Second Advisor

Dr. Sheikh Ariful Islam

Third Advisor

Dr. Honglu Jiang


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.


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