Privacy-preserving neural networks have attracted increasing attention in recent years, and various algorithms have been developed to keep the balance between accuracy, computational complexity and information security from the cryptographic view. This work takes a different view from the input data and structure of neural networks. We decompose the input data (e.g., some images) into sensitive and insensitive segments according to importance and privacy. The sensitive segment includes some important and private information such as human faces and we take strong homomorphic encryption to keep security, whereas the insensitive one contains some background and we add perturbations. We propose the bi-CryptoNets, i.e., plaintext and ciphertext branches, to deal with two segments, respectively, and ciphertext branch could utilize the information from plaintext branch by unidirectional connections. We adopt knowledge distillation for our bi-CryptoNets by transferring representations from a well-trained teacher neural network. Empirical studies show the effectiveness and decrease of inference latency for our bi-CryptoNets.

翻译：近年来，隐私保护的神经网络日益受到关注，研究者从密码学视角提出了多种算法以平衡准确性、计算复杂度与信息安全。本文从输入数据及神经网络结构层面另辟蹊径。我们根据重要性与隐私性将输入数据（如图像）分解为敏感与非敏感片段：敏感片段包含人脸等重要隐私信息，采用强同态加密保障安全性；非敏感片段则包含背景信息，通过添加扰动进行处理。我们提出双分支的Bi-CryptoNets（即明文分支与密文分支），分别处理两类数据片段，其中密文分支可通过单向连接利用明文分支的信息。我们采用知识蒸馏技术，将预训练教师神经网络的知识表征迁移至Bi-CryptoNets。实验表明，该方法在保证有效性的同时显著降低了推理延迟。