Abstract
In recent years, the problem of privacy leakage has attracted increasing attentions. Therefore, machine learning privacy protection becomes crucial research topic. In this paper, the Paillier homomorphic encryption algorithm is proposed to protect the privacy data. The original LeNet-5 convolutional neural network model was first improved. Then the activation function was modified and the C5 layer was removed to reduce the number of model parameters and improve the operation efficiency. Finally, by mapping the operation of each layer in the convolutional neural network from the plaintext domain to the ciphertext domain, an improved LeNet-5 model that can run on encrypted data was constructed. The purpose of using machine learning algorithm was realized and privacy was ensured at the same time. The analysis shows that the model is feasible and the efficiency is improved.
Foundation Items:
The National Natural Science Foundation of China (No.61572521), Engineering University of PAP Innovation Team Science Foundation (No. KYTD201805), Natural Science Basic Research Plan in Shaanxi Province of China (2021JM252).
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Ni, H., Han, Y., Duan, X., Yang, G. (2021). An Improved LeNet-5 Model Based on Encrypted Data. In: Zeng, J., Qin, P., Jing, W., Song, X., Lu, Z. (eds) Data Science. ICPCSEE 2021. Communications in Computer and Information Science, vol 1452. Springer, Singapore. https://doi.org/10.1007/978-981-16-5943-0_14
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DOI: https://doi.org/10.1007/978-981-16-5943-0_14
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