VeriCNN: Integrity verification of large-scale CNN training process based on zk-SNARK

Yongkai Fan, Kaile Ma, Linlin Zhang, Jiqiang Liu, Naixue Xiong, Shui Yu

Abstract: Cloud environments are frequently employed to train Convolutional Neural Networks (CNNs). On the other hand, the training procedure is exposed to potential integrity threats due to the cloud’s lack of trustworthiness. Although there are now available methods for zero-knowledge proof-based training integrity verification for small models, the low-proof performance of large-scale CNNs like LeNet-5 and VGG16 makes this task challenging. To solve the training integrity verification of large-scale CNNs, this research suggests a technique called VeriCNN. Particularly, VeriCNN builds proof of integrity for CNN training using zk-SNARK. It adopts the Winograd algorithm to optimize convolution operations and introduces a high-probability matrix multiplication to optimize zero-knowledge proofs. Experimental results demonstrate the effectiveness and practicality of VeriCNN in verifying the training integrity of large-scale models. For instance, VeriCNN can generate a training integrity proof for the LeNet-5 model in just 25 s and for the VGG16 model in 390 s.