SilentWood: Efficient Private Inference Over Gradient-Boosting Decision Forests
Gradient boosting decision forests are accurate but slow for private inference. SilentWood proposes an efficient protocol using homomorphic encryption to speed up this process by optimizing how trees are combined. It achieves significant improvements in communication and computation costs over naive methods through three specific techniques: computation clustering, blind code conversion, and ciphertext compression.