支持隐私保护训练的高效同态神经网络

针对基于同态加密的隐私保护神经网络中存在的计算效率低和精度不足问题，提出一种三方协作下支持隐私保护训练的高效同态神经网络（HNN）。首先，为降低同态加密中密文乘密文运算产生的计算开销，结合秘密共享思想设计了一种安全快速的乘法协议，将密文乘密文运算转换为复杂度较低的明文乘密文运算；其次，为避免构建HNN时产生的密文多项式多轮迭代，并提高非线性计算精度，研究了一种安全的非线性计算方法，从而对添加随机掩码的混淆明文消息执行相应的非线性算子；最后，对所设计协议的安全性、正确性及效率进行了理论分析，并对HNN的有效性及优越性进行了实验验证。实验结果表明，相较于双服务器方案PPML,HNN的训练速度提高了18.9倍，模型精度提高了1.4个百分点。

Efficient homomorphic neural network supporting privacy-preserving training

Aiming at the problems of low computational efficiency and insufficient accuracy in the privacy-preserving neural network based on homomorphic encryption， an efficient Homomorphic Neural Network （HNN） under three-party collaborative supporting privacy-preserving training was proposed. Firstly， in order to reduce the computational cost of ciphertext-ciphertext multiplication in homomorphic encryption， the idea of secret sharing was combined to design a secure fast multiplication protocol to convert the ciphertext-ciphertext multiplication into plaintext-ciphertext multiplication with low complexity. Then， in order to avoid multiple iterations of ciphertext polynomials generated during the construction of HNN and improve the nonlinear calculation accuracy， a secure nonlinear calculation method was studied， which executed the corresponding nonlinear operator for the confused plaintext message with random mask. Finally， the security， correctness and efficiency of the proposed protocols were analyzed theoretically， and the effectiveness and superiority of HNN were verified by experiments. Experimental results show that compared with the dual server scheme PPML （Privacy Protection Machine Learning）， HNN has the training efficiency improved by 18.9 times and the model accuracy improved by 1.4 percentage points.