Secure Convolution Neural Network Inference Based on Homomorphic Encryption
Today, the rapid development of deep learning has spread across all walks of life, and it can be seen in various fields such as image classification, automatic driving, and medical imaging diagnosis. Convolution Neural Networks (CNNs) are also widely used by the public as tools for deep learning. In...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-05-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/10/6117 |
| _version_ | 1827742163832668160 |
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| author | Chen Song Ruwei Huang |
| author_facet | Chen Song Ruwei Huang |
| author_sort | Chen Song |
| collection | DOAJ |
| description | Today, the rapid development of deep learning has spread across all walks of life, and it can be seen in various fields such as image classification, automatic driving, and medical imaging diagnosis. Convolution Neural Networks (CNNs) are also widely used by the public as tools for deep learning. In real life, if local customers implement large-scale model inference first, they need to upload local data to the cloud, which will cause problems such as data leakage and privacy disclosure. To solve this problem, we propose a framework using homomorphic encryption technology. Our framework has made improvements to the batch operation and reduced the complexity of layer connection. In addition, we provide a new perspective to deal with the impact of the noise caused by the homomorphic encryption scheme on the accuracy during the inference. In our scheme, users preprocess the images locally and then send them to the cloud for encrypted inference without worrying about privacy leakage during the inference process. Experiments show that our proposed scheme is safe and efficient, which provides a safe solution for users who cannot process data locally. |
| first_indexed | 2024-03-11T03:58:20Z |
| format | Article |
| id | doaj.art-15b8d141a30e4d99a873885157a4336c |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T03:58:20Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-15b8d141a30e4d99a873885157a4336c2023-11-18T00:20:49ZengMDPI AGApplied Sciences2076-34172023-05-011310611710.3390/app13106117Secure Convolution Neural Network Inference Based on Homomorphic EncryptionChen Song0Ruwei Huang1School of Computer and Electronic Information, Guangxi University, Nanning 530004, ChinaSchool of Computer and Electronic Information, Guangxi University, Nanning 530004, ChinaToday, the rapid development of deep learning has spread across all walks of life, and it can be seen in various fields such as image classification, automatic driving, and medical imaging diagnosis. Convolution Neural Networks (CNNs) are also widely used by the public as tools for deep learning. In real life, if local customers implement large-scale model inference first, they need to upload local data to the cloud, which will cause problems such as data leakage and privacy disclosure. To solve this problem, we propose a framework using homomorphic encryption technology. Our framework has made improvements to the batch operation and reduced the complexity of layer connection. In addition, we provide a new perspective to deal with the impact of the noise caused by the homomorphic encryption scheme on the accuracy during the inference. In our scheme, users preprocess the images locally and then send them to the cloud for encrypted inference without worrying about privacy leakage during the inference process. Experiments show that our proposed scheme is safe and efficient, which provides a safe solution for users who cannot process data locally.https://www.mdpi.com/2076-3417/13/10/6117convolution neural networkcloud computinghomomorphic encryptionprivacy preserving machine learningCKKS FHE scheme |
| spellingShingle | Chen Song Ruwei Huang Secure Convolution Neural Network Inference Based on Homomorphic Encryption Applied Sciences convolution neural network cloud computing homomorphic encryption privacy preserving machine learning CKKS FHE scheme |
| title | Secure Convolution Neural Network Inference Based on Homomorphic Encryption |
| title_full | Secure Convolution Neural Network Inference Based on Homomorphic Encryption |
| title_fullStr | Secure Convolution Neural Network Inference Based on Homomorphic Encryption |
| title_full_unstemmed | Secure Convolution Neural Network Inference Based on Homomorphic Encryption |
| title_short | Secure Convolution Neural Network Inference Based on Homomorphic Encryption |
| title_sort | secure convolution neural network inference based on homomorphic encryption |
| topic | convolution neural network cloud computing homomorphic encryption privacy preserving machine learning CKKS FHE scheme |
| url | https://www.mdpi.com/2076-3417/13/10/6117 |
| work_keys_str_mv | AT chensong secureconvolutionneuralnetworkinferencebasedonhomomorphicencryption AT ruweihuang secureconvolutionneuralnetworkinferencebasedonhomomorphicencryption |