TeeBench: Seamless Benchmarking in Trusted Execution Environments
Kajetan Maliszewski, Tilman Dietzel, Jorge-Arnulfo Quiané-Ruiz, Volker Markl · 2023
Trusted Execution Environments (TEEs) have enabled building secure systems that operate on untrusted machines. However, TEEs' architecture questions previous performance findings. The existing relational algorithms have been designed for traditional CPUs. Prior work has shown that these algorithms underperform in TEEs and, in most cases, can not be easily reused. Moreover, they frequently used benchmarks pertinent to CPUs and ignored TEE-specific metrics essential to understand the performance differences. Therefore, there is a need for a fair benchmarking approach for TEE algorithms.