13 citations · 26 across the 5 of their papers we have counts for
5 papers
Optimized Layerwise Approximation for Efficient Private Inference on Fully Homomorphic Encryption
Junghyun Lee, Eunsang Lee, Young-Sik Kim +4
Recent studies have explored the deployment of privacy-preserving deep neural networks utilizing homomorphic encryption (HE), especially for private inference (PI). Many works have…
Privacy-Preserving Machine Learning with Fully Homomorphic Encryption for Deep Neural Network
Joon-Woo Lee, HyungChul Kang, Yongwoo Lee +8
Fully homomorphic encryption (FHE) is one of the prospective tools for privacypreserving machine learning (PPML), and several PPML models have been proposed based on various FHE sc…
Precise Approximation of Convolutional Neural Networks for Homomorphically Encrypted Data
Junghyun Lee, Eunsang Lee, Joon-Woo Lee +3
Homomorphic encryption is one of the representative solutions to privacy-preserving machine learning (PPML) classification enabling the server to classify private data of clients w…
Analysis of error dependencies on NewHope
Minki Song, Seunghwan Lee, Eunsang Lee +3
Among many submissions to the NIST post-quantum cryptography (PQC) project, NewHope is a promising key encapsulation mechanism (KEM) based on the Ring-Learning with errors (Ring-LW…
Improving security and bandwidth efficiency of NewHope using error-correction schemes
Minki Song, Seunghwan Lee, Eunsang Lee +3
Among many submissions to the NIST post-quantum cryptography (PQC) project, NewHope is a promising key encapsulation mechanism (KEM) based on the Ring-Learning with errors (Ring-LW…