提出高效可验证的卷积计算方案，保护云端推理隐私且提速87倍
Efficient and Verifiable Privacy-Preserving Convolutional Computation for CNN Inference with Untrusted Clouds
- 用同态加密与秘密共享实现卷积层安全计算
- 在10个数据集上提速26~87倍，精度无损
- 适合资源受限设备做隐私保护的云推理
卷积神经网络（CNN）在资源受限场景的广泛应用推动了机器学习即服务（MLaaS）的发展。然而，客户端向不可信云服务器传输数据时易造成隐私泄露。现有基于同态加密和秘密共享的隐私保护方案虽能保障数据机密性，但在卷积操作上存在效率瓶颈。本文提出一种专为CNN卷积层设计的新颖可验证隐私保护方案，支持高效加解密，使资源受限客户端可安全地将计算任务外包给不可信云服务器。同时，我们设计了一种验证机制，可以至少 $1- rac{1}{ig|Zig|}$ 的成功率检测结果正确性。在10个数据集及多种CNN模型上的大量实验表明，该方案相比原始明文模型提速26~87倍，且保持原有精度。
原文摘要 · Abstract (English)
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.
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