Efficient and Verifiable Privacy-Preserving Convolutional Computation for CNN Inference with Untrusted Clouds

The widespread adoption of convolutional neural networks CNNs in resource-constrained scenarios has driven the development of Machine Learning as a Service MLaaS system. However, this approach is susceptible to privacy leakage, as the data sent from the client to the untrusted cloud server often contains sensitive information. Existing CNN privacy-preserving schemes, while effective in ensuring data confidentiality through homomorphic encryption and secret sharing, face efficiency bottlenecks, particularly in convolution operations. In this paper, we propose a novel verifiable privacy-preserving scheme tailored for CNN convolutional layers. Our scheme enables efficient encryption and decryption, allowing resource-constrained clients to securely offload computations to the untrusted cloud server. Additionally, we present a verification mechanism capable of detecting the correctness of the results with a success probability of at least 1-1 Z. Extensive experiments conducted on 10 datasets and various CNN architectures demonstrate that our scheme achieves speedups ranging from 26× to 87× compared to the original plaintext model while maintaining accuracy.