Privacy-preserving machine learning solutions have recently gained
significant attention. One promising research trend is using Homomorphic
Encryption (HE), a method for performing computation over encrypted data. One
major challenge in this approach is training HE-friendly, encrypted or
unencrypted, deep CNNs with decent accuracy. We propose a novel training method
for HE-friendly models, and demonstrate it on fundamental and modern CNNs, such
as ResNet and ConvNeXt. After training, we evaluate our models by running
encrypted samples using HELayers SDK and proving that they yield the desired
results. When running on a GPU over the ImageNet dataset, our ResNet-18/50/101
implementations take only 7, 31 and 57 minutes, respectively, which shows that
this solution is practical. Furthermore, we present several insights on
handling the activation functions and skip-connections under HE. Finally, we
demonstrate in an unprecedented way how to perform secure zero-shot prediction
using a CLIP model that we adapted to be HE-friendly.

本文提出了一种新的 HE-friendly 模型的训练方法，展示了如何使用 HC Layers SDK 运行加密样本，并证明该模型可以在保持高准确度的同时，大大节省运行时间；研究人员还讨论了在 HE 条件下处理激活函数和跳过连接的不同策略，并且通过改编 CLIP 模型实现了安全的零 - shot 预测。