  This work presents a novel protocol for fast secure inference of neural
networks applied to computer vision applications. It focuses on improving the
overall performance of the online execution by deploying a subset of the model
weights in plaintext on the client's machine, in the fashion of SplitNNs. We
evaluate our protocol on benchmark neural networks trained on the CIFAR-10
dataset using SEAL via TenSEAL and discuss runtime and security performances.
Empirical security evaluation using Membership Inference and Model Extraction
attacks showed that the protocol was more resilient under the same attacks than
a similar approach also based on SplitNN. When compared to related work, we
demonstrate improvements of 2.5x-10x for the inference time and 14x-290x in
communication costs.
