Accelerating Number Theoretic Transformations for Bootstrappable Homomorphic Encryption on GPUs. - Sangpyo Kim; Wonkyung Jung; Jaiyoung Park; Jung Ho Ahn (IISWC, 2020)

AESPA: Accuracy Preserving Low-degree Polynomial Activation for Fast Private Inference. - Jaiyoung Park; Michael Jaemin Kim; Wonkyung Jung; Jung Ho Ahn (CoRR, 2022)

SHARP: A Short-Word Hierarchical Accelerator for Robust and Practical Fully Homomorphic Encryption. - Jongmin Kim 007; Sangpyo Kim; Jaewan Choi; Jaiyoung Park; Donghwan Kim; Jung Ho Ahn (ISCA, 2023)

HyPHEN: A Hybrid Packing Method and Optimizations for Homomorphic Encryption-Based Neural Networks. - Donghwan Kim; Jaiyoung Park; Jongmin Kim 007; Sangpyo Kim; Jung Ho Ahn (CoRR, 2023)

Toward Practical Privacy-Preserving Convolutional Neural Networks Exploiting Fully Homomorphic Encryption. - Jaiyoung Park; Donghwan Kim; Jongmin Kim 007; Sangpyo Kim; Wonkyung Jung; Jung Hee Cheon; Jung Ho Ahn (CoRR, 2023)

NeuJeans: Private Neural Network Inference with Joint Optimization of Convolution and Bootstrapping. - Jae Hyung Ju; Jaiyoung Park; Jongmin Kim 007; Donghwan Kim; Jung Ho Ahn (CoRR, 2023)

NeuJeans: Private Neural Network Inference with Joint Optimization of Convolution and FHE Bootstrapping. - Jae Hyung Ju; Jaiyoung Park; Jongmin Kim 007; Minsik Kang; Donghwan Kim; Jung Hee Cheon; Jung Ho Ahn (CCS, 2024)

HyPHEN: A Hybrid Packing Method and Its Optimizations for Homomorphic Encryption-Based Neural Networks. - Donghwan Kim; Jaiyoung Park; Jongmin Kim 007; Sangpyo Kim; Jung Ho Ahn (IEEE Access, 2024)