Efficient Secure Inference Scheme for Large Neural Networks

Secure inference is the main technology to avoid privacy leakage when reasoning with machine learning models. The existing secure inference schemes have been widely explored in the academic field, but when the neural network model is large, existing solutions based on Trusted Execution Environments (TEE) must be configured with a large number of trusted devices due to insufficient hardware memory, which incurs high deployment costs. In addition, existing homomorphic encryption schemes have a high computational cost for ciphertexts, which incurs high time cost when the model is large. To address these issues, we propose a low time overhead secure inference scheme by training a generative adversarial network, which can reduce the calculations for ciphertext. We also use a trusted execution environment to compute nonlinear functions in neural networks, which solves the problem of high computational cost for nonlinear functions in HE while suppressing the growth of noise in HE. Security analysis proves that our scheme achieves semi-honest security, and extensive experiments demonstrate that our scheme has lower time overhead in large neural networks when compare with previous solutions.