


We search for a student to measure the overhead of confidential computing and identify bottlenecks in competing implementations of confidential computing. The hardware and open-source repositories are available, but require the setup of a testbed and evaluation. Ideally, overheads are measured, analyzed, and reduced by improved operations, parallelization, etc. Confidential computing is a promising technology for preserving data privacy by protecting data in use on shared hardware. The implementation of confidential computing is not standardized yet and depends on the manufacturer, e.g., AMD and Intel. Generally, confidential computing utilizes symmetric encryption algorithms within the GPU or CPU to preserve data privacy.