Predicting the Execution Time of Secure Neural Network Inference

Zhang, E.; Mann, Z. Á.

Abstract

In the secure neural network inference (SNNI) problem, a service provider offers inference as a service with a pre-trained neural network (NN). Clients can use the service by providing an input and obtaining the output of the inference with the NN. For reasons of privacy and intellectual property protection, the service provider must not learn anything about the input or the output, and the client must not learn anything about the internal parameters of the NN. This is possible by applying techniques like multi-party computing (MPC) or homomorphic encryption (HE), although with a significant performance overhead. One way to improve the efficiency of SNNI is by selecting NN architectures that can be evaluated faster using MPC or HE. For this, it would be important to predict how long SNNI with a given NN takes. This turns out to be challenging. Traditional predictors for NN inference time, like the number of parameters in the NN, are poor predictors of SNNI execution time, since they ignore the characteristics of cryptographic protocols. This paper is the first to address this problem. We propose three different prediction methods for SNNI execution time, and investigate experimentally their strengths and weaknesses. The results show that the proposed methods offer different advantages in terms of accuracy and speed.