论文标题
神经网络培训具有同态加密
Neural Network Training With Homomorphic Encryption
论文作者
论文摘要
我们介绍了一种新颖的方法和实施体系结构，以训练神经网络，以保留模型和数据的机密性。我们的方法依赖于基于晶格的加密方案的同态能力。为了实现模型参数的有效更新，我们的过程已针对包装的密文进行了优化。由于我们在从馈电网络到后传播网络上执行乘法和旋转的方式，我们的方法大大降低了计算。为了验证训练模型的准确性以及实施可行性，我们通过将Microsoft Seal的CKKS方案作为后端测试了IRIS数据集的方法。尽管我们的测试实施是用于简单的神经网络培训，但我们认为我们的基本实现块可以帮助更复杂的基于神经网络的用例的进一步应用程序。
We introduce a novel method and implementation architecture to train neural networks which preserves the confidentiality of both the model and the data. Our method relies on homomorphic capability of lattice based encryption scheme. Our procedure is optimized for operations on packed ciphertexts in order to achieve efficient updates of the model parameters. Our method achieves a significant reduction of computations due to our way to perform multiplications and rotations on packed ciphertexts from a feedforward network to a back-propagation network. To verify the accuracy of the training model as well as the implementation feasibility, we tested our method on the Iris data set by using the CKKS scheme with Microsoft SEAL as a back end. Although our test implementation is for simple neural network training, we believe our basic implementation block can help the further applications for more complex neural network based use cases.
