Efficient and Verifiable Privacy-Preserving Convolutional Computation for CNN Inference with Untrusted Clouds
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866909742659534848 |
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| author | Lu, Jinyu Sun, Xinrong Tao, Yunting Ji, Tong Kong, Fanyu Yang, Guoqiang |
| author_facet | Lu, Jinyu Sun, Xinrong Tao, Yunting Ji, Tong Kong, Fanyu Yang, Guoqiang |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_12832 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Efficient and Verifiable Privacy-Preserving Convolutional Computation for CNN Inference with Untrusted Clouds Lu, Jinyu Sun, Xinrong Tao, Yunting Ji, Tong Kong, Fanyu Yang, Guoqiang Cryptography and Security Machine Learning 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. |
| title | Efficient and Verifiable Privacy-Preserving Convolutional Computation for CNN Inference with Untrusted Clouds |
| topic | Cryptography and Security Machine Learning |
| url | https://arxiv.org/abs/2508.12832 |