PPCNN: An efficient privacy‐preserving CNN training and inference framework
Fan Zhao, Zhi Li, Hao Wang · International Journal of Intelligent Systems · 2022
Convolutional neural network (CNN) is one of the representative models of deep learning, commonly used to analyze visual images. CNN model is more accurate when trained on large amounts of data from multiple sources, and the huge training cost makes the model much more valuable. However, data from various sources is often privacy-sensitive. Therefore, the privacy of these data should be protected during CNN model training and inference. In this paper, we propose an efficient and secure two-party computation (2PC) framework PPCNN for privacy-preserving CNN training and inference. Specifically, we use a new secret sharing technique introduced in ABY2.0 to securely compute various computational tasks involved in the CNN training and inference processes. This secret sharing technique can significantly reduce the communication overhead. Meanwhile, we assign these computationally intensive tasks to cloud servers to reduce the computational burden on local devices. We demonstrate the security of these protocols in the semihonest model. In addition, we use the MP-SPDZ library to simulate our PPCNN framework, and the experiments prove its high efficiency and accuracy.