Verification of Deep Neural Networks with KGZ-Based zkSNARK

Verification of a deep neural network is required as large DNN models are used in machine learning as a service procedure where the server providing a classification service may be insecure and provide invalid classifications. A verification of deep neural networks in a machine learning as a service paradigm requires verification of function evaluation for all functions of a DNN model given a specific input where the service provider and the server do not want to reveal the DNN model to the client. In this paper, we investigate the privacy-preserving verification problem of the DNN model with zero-knowledge proofs. We have developed a KGZ polynomial commitment scheme based on zero-knowledge proof for such DNN verification. We present an efficient DNN verification using KGZ zero-knowledge proof. We have developed a batch-processing algorithm that can significantly reduce the number of function evaluation verifications. We also prove that a malicious server may not manipulate the proposed verification protocol.