Privacy-Preserving Deep Learning Using Deformable Operators for Secure Task Learning

Perez, Fabian; Lopez, Jhon; Arguello, Henry

doi:10.1109/icassp48485.2024.10446218

ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2024

DOI: 10.1109/icassp48485.2024.10446218

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Privacy-Preserving Deep Learning Using Deformable Operators for Secure Task Learning

Fabian Perez,

Jhon Lopez,

Henry Arguello

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2024

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Privacy-Preserving ConvMixer Without Any Accuracy Degradation Using Compressible Encrypted Images

Lin,

Imaizumi,

Kiya

2024

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We propose an enhanced privacy-preserving method for image classification using ConvMixer, which is an extremely simple model that is similar in spirit to the Vision Transformer (ViT). Most privacy-preserving methods using encrypted images cause the performance of models to degrade due to the influence of encryption, but a state-of-the-art method was demonstrated to have the same classification accuracy as that of models without any encryption under the use of ViT. However, the method, in which a common secret key is assigned to each patch, is not robust enough against ciphertext-only attacks (COAs) including jigsaw puzzle solver attacks if compressible encrypted images are used. In addition, ConvMixer is less robust than ViT because there is no position embedding. To overcome this issue, we propose a novel block-wise encryption method that allows us to assign an independent key to each patch to enhance robustness against attacks. In experiments, the effectiveness of the method is verified in terms of image classification accuracy and robustness, and it is compared with conventional privacy-preserving methods using image encryption.

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Privacy-Preserving ConvMixer Without Any Accuracy Degradation Using Compressible Encrypted Images

Lin,

Imaizumi,

Kiya

2024

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Privacy-Preserving Deep Learning Using Deformable Operators for Secure Task Learning

Cited by 1 publication

References 16 publications

Privacy-Preserving ConvMixer Without Any Accuracy Degradation Using Compressible Encrypted Images

Privacy-Preserving ConvMixer Without Any Accuracy Degradation Using Compressible Encrypted Images

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