We present a secure backpropagation neural network training model (SecureBP), which allows a neural network to be trained while retaining the confidentiality of the training data, based on the homomorphic encryption scheme. We make two contributions. The first one is to introduce a method to find a more accurate and numerically stable polynomial approximation of functions in a certain interval. The second one is to find a strategy of refreshing ciphertext during training, which keeps the order of magnitude of noise at .

中文翻译：

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来自同态加密的SecureBP

我们提出了一种基于同态加密方案的安全反向传播神经网络训练模型（SecureBP），该模型允许对神经网络进行训练，同时保留训练数据的机密性。我们做出两个贡献。第一个方法是引入一种方法，以在一定间隔内找到函数的更准确和数值稳定的多项式逼近。第二个方法是找到一种在训练期间刷新密文的策略，该策略将噪声的数量级保持在。