no code implementations • 7 Dec 2023 • Jae Hyung Ju, Jaiyoung Park, Jongmin Kim, Donghwan Kim, Jung Ho Ahn
NeuJeans accelerates the performance of conv2d by up to 5. 68 times compared to state-of-the-art FHE-based PI work and performs the PI of a CNN at the scale of ImageNet (ResNet18) within a mere few seconds
no code implementations • 5 Feb 2023 • Donghwan Kim, Jaiyoung Park, Jongmin Kim, Sangpyo Kim, Jung Ho Ahn
Convolutional neural network (CNN) inference using fully homomorphic encryption (FHE) is a promising private inference (PI) solution due to the capability of FHE that enables offloading the whole computation process to the server while protecting the privacy of sensitive user data.
no code implementations • 18 Jan 2022 • Jaiyoung Park, Michael Jaemin Kim, Wonkyung Jung, Jung Ho Ahn
We apply AESPA to popular ML models, such as VGGNet, ResNet, and pre-activation ResNet, to show an inference accuracy comparable to those of the standard models with ReLU activation, achieving superior accuracy over prior low-degree polynomial studies.
no code implementations • 3 Dec 2020 • Sangpyo Kim, Wonkyung Jung, Jaiyoung Park, Jung Ho Ahn
Homomorphic encryption (HE) draws huge attention as it provides a way of privacy-preserving computations on encrypted messages.
Cryptography and Security Distributed, Parallel, and Cluster Computing