Date of Award
Master of Science (MS)
Dr. Lei Xu
Dr. Sheikh Ariful Islam
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.
Hall, Lucas D., "Hardware Isolation Approach to Securely Use Untrusted GPUS in Cloud Environments for Machine Learning" (2022). Theses and Dissertations. 1051.