Neural Network Training With Homomorphic Encryption [article]

Kentaro Mihara, Ryohei Yamaguchi, Miguel Mitsuishi, Yusuke Maruyama
2020 arXiv   pre-print
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
more » ... om 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.
arXiv:2012.13552v1 fatcat:sp3ef7wc3zeqfnbfs7ologujhm