VSecNN: Verifiable and Privacy-Preserving Neural Network Inference in Cloud Service
Wenti Yang, Xuan Li, Meng Li, Zijian Zhang, Zhitao Guan, Liehuang Zhu · IEEE Transactions on Information Forensics and Security · 2025
Neural network inference in cloud service offers tangible benefits to users, from individuals and small institutions to large companies. However, two crucial concerns must be addressed. The first arises in satisfying the privacy of the model, the input data, and the inference results throughout the inference process. The second pertains to verifying that the inferences are derived from the designated neural network model. Although Secure Multi-Party Computation (MPC) and Zero-Knowledge Proof (ZKP) are typically adopted to mitigate such issues, the major challenge lies in achieving privacy preservation and verifiability simultaneously. In this study, we address both issues by proposing VSecNN, a verifiable and privacy-preserving neural network inference scheme. Specifically, we integrate MPC with the Zero-Knowledge Succinct Non-Interactive Argument of Knowledge (zk-SNARK) protocol to achieve zero-knowledge proof generation for multiple parties. Subsequently, we perform adaptive optimizations on the multi-party proof generation approach to align with the neural network, thereby achieving both privacy-preserving capabilities and verifiability. Experimental results demonstrate an improvement in the efficiency. For example, the computation time for completing our multi-party proof generation could be as low as 1.7 times that of the single-party proof generation, while the verification requires only 169ms on the MNIST dataset.