180 citations · 503 across the 15 of their papers we have counts for
4 papers · 1 filter
NeuJeans: Private Neural Network Inference with Joint Optimization of Convolution and FHE Bootstrapping
Jae Hyung Ju, Jaiyoung Park, Jongmin Kim +4
Fully homomorphic encryption (FHE) is a promising cryptographic primitive for realizing private neural network inference (PI) services by allowing a client to fully offload the inf…
Toward Practical Privacy-Preserving Convolutional Neural Networks Exploiting Fully Homomorphic Encryption
Jaiyoung Park, Donghwan Kim, Jongmin Kim +4
Incorporating fully homomorphic encryption (FHE) into the inference process of a convolutional neural network (CNN) draws enormous attention as a viable approach for achieving priv…
CiFHER: A Chiplet-Based FHE Accelerator with a Resizable Structure
Sangpyo Kim, Jongmin Kim, Jaeyoung Choi +1
Fully homomorphic encryption (FHE) is in the spotlight as a definitive solution for privacy, but the high computational overhead of FHE poses a challenge to its practical adoption.…
HyPHEN: A Hybrid Packing Method and Optimizations for Homomorphic Encryption-Based Neural Networks
Donghwan Kim, Jaiyoung Park, Jongmin Kim +2
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 offlo…