01340nam a2200145 a 450000500170000000800400001710000190005724500560007626400160013233600090014833800200015752009050017750600460108285600660112820260901030331.0250101s2020    xx     o     000 0 eng d1 aKentaro Mihara10aNeural Network Training With Homomorphic Encryption 1barXivc2020  atext  aonline resource  aWe 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 mo0 aOpen access — freely available to read.40uhttps://arxiv.org/pdf/2012.13552v1yRead the full paper (PDF)