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ValidCNN: A Large-Scale CNN Predictive Integrity Verification Scheme Based on zk-SNARK | IEEE Journals & Magazine | IEEE Xplore

ValidCNN: A Large-Scale CNN Predictive Integrity Verification Scheme Based on zk-SNARK


Abstract:

The integrity of cloud-based convolutional neural network (CNN) prediction services can be jeopardized by a malicious cloud server. Although zero-knowledge proof approach...Show More

Abstract:

The integrity of cloud-based convolutional neural network (CNN) prediction services can be jeopardized by a malicious cloud server. Although zero-knowledge proof approaches can be used to verify integrity, they are difficult to use for larger CNN models like LeNet-5 and VGG16, due to the large cost (in terms of time and storage) of generating a proof. This paper proposes ValidCNN, which can efficiently generate integrity proofs based on zk-SNARK. At the heart of ValidCNN, it is a novel usage of Freivald's concepts for circuit construction, and a more efficient way for verifying matrix multiplication. Our experimental results demonstrate that ValidCNN significantly outperforms the state of the art approaches that are based on zk-SNARK. For example, compared with ZEN, ValidCNN achieves a 12-fold improvement in time and a 31-fold improvement in storage. Compared with vCNN, ValidCNN achieves a 195-fold and 279-fold improvement in time and storage respectively.
Published in: IEEE Transactions on Dependable and Secure Computing ( Volume: 21, Issue: 6, Nov.-Dec. 2024)
Page(s): 5185 - 5195
Date of Publication: 29 February 2024

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