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pvCNN: Privacy-Preserving and Verifiable Convolutional Neural Network Testing
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2022
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This paper proposes a new approach for privacy-preserving and verifiable convolutional neural network (CNN) testing, enabling a CNN model developer to convince a user of the truthful CNN performance over ...
To balance the security and efficiency issues, three new efforts are done by appropriately integrating homomorphic encryption (HE) and zero-knowledge succinct non-interactive argument of knowledge (zk-SNARK ...
INTRODUCTION Convolutional neural networks (CNNs) [1] , [2] have been widely applied in various application scenarios, such as healthcare analysis, autonomous vehicle and face recognition. ...
doi:10.48550/arxiv.2201.09186
fatcat:nizbstndaraglhzbb6xp4c22re