{"ID":2881084,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.12832","arxiv_id":"2508.12832","title":"Efficient and Verifiable Privacy-Preserving Convolutional Computation for CNN Inference with Untrusted Clouds","abstract":"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 $1-\\frac{1}{\\left|Z\\right|}$. Extensive experiments conducted on 10 datasets and various CNN models demonstrate that our scheme achieves speedups ranging $26 \\times$ ~ $\\ 87\\times$ compared to the original plaintext model while maintaining accuracy.","short_abstract":"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...","url_abs":"https://arxiv.org/abs/2508.12832","url_pdf":"https://arxiv.org/pdf/2508.12832v2","authors":"[\"Jinyu Lu\",\"Xinrong Sun\",\"Yunting Tao\",\"Tong Ji\",\"Fanyu Kong\",\"Guoqiang Yang\"]","published":"2025-08-18T11:17:53Z","proceeding":"cs.CR","tasks":"[\"cs.CR\",\"cs.LG\"]","methods":"[\"Convolutional Neural Network\"]","has_code":false}