microsoft/private-benchmarking

With only 13 stars and 2 forks over nearly two years, this project lacks any market momentum or community adoption. It appears to be a 'code dump' associated with a Microsoft Research paper rather than a maintained product. The defensibility is extremely low as the code serves primarily as a reference for how to use Azure Confidential Computing primitives for benchmarking. While the problem of private model evaluation is significant, especially for regulated industries (finance, healthcare), the current implementation is effectively a skeleton. Competitors like Mithril Security (BlindLlama) or Cape Privacy have built far more robust, user-friendly layers for secure inference. Furthermore, the risk of platform domination is high because Microsoft is likely to bake these exact capabilities directly into Azure Machine Learning or Azure Confidential Computing as a native service, rendering this standalone repository obsolete. The zero velocity indicates it is no longer being actively developed.