Privacy-preserving convolutional neural network inference scheme based on homomorphic ciphertext transformation
To solve the problems of frequent interaction and low prediction accuracy of existing privacy-protected convo-lutional neural networks,a homomorphic friendly non-interactive privacy-protected convolutional neural network infer-ence scheme was proposed via homomorphic ciphertext transformation.Utilizing the Pegasus framework,CKKS(Cheon-Kim-Kim-Song)ciphertext was used to parallelize convolution operations in convolution layer.In the activation layer and pooling layer,LWE ciphertext and LUT(look-up table)technology were used to calculate the activation func-tion,maximum pooling and global pooling.Using the ciphertext conversion technology provided by the Pegasus frame-work,the conversion between different forms of homomorphic ciphertext is realized.Theoretical analysis and experimen-tal results show that the proposed scheme can ensure data security,and has higher inference accuracy and lower calcula-tion and communication overhead.
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