01386nam a2200169 a 4500 20260901030331.0 250101s2020 xx o 000 0 eng d Kentaro Mihara Neural Network Training With Homomorphic Encryption arXiv 2020 text online resource 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 mo Open access — freely available to read. https://arxiv.org/pdf/2012.13552v1 Read the full paper (PDF) ERES 645 645 0 0 0 3 CYB MAIN MAIN SCICOMP 2026-09-01 0 YGE000906 2026-09-01 03:03:31 https://arxiv.org/pdf/2012.13552v1 2026-09-01 PAPER