180 citations · 503 across the 15 of their papers we have counts for
11 papers · 1 filter
WHET: Welding Homomorphic Encryption to Accelerator Architectures
Jongmin Kim, Hyesung Ji, Wonseok Choi +2
Fully homomorphic encryption (FHE) enables computations on encrypted data without decryption, offering strong data privacy at the expense of substantial computational and memory ov…
VIPIR: A Versatile GPU Framework for Integrating Private Information Retrieval Protocols
Jongmin Kim, Hyesung Ji, Jean-Luc Watson +3
While private information retrieval (PIR) enables private database services by fully concealing access patterns, it simultaneously requires high computational throughput, large mem…
GPIR: Enabling Practical Private Information Retrieval with GPUs
Hyesung Ji, Hyunah Yu, Jongmin Kim +3
Private information retrieval (PIR) allows private database queries; however, it is hindered by intense server-side computation and memory traffic. Numerous modern lattice-based PI…
Theodosian: A Deep Dive into Memory-Hierarchy-Centric FHE Acceleration
Wonseok Choi, Hyunah Yu, Jongmin Kim +3
Fully homomorphic encryption (FHE) enables secure computation on encrypted data, mitigating privacy concerns in cloud and edge environments. However, due to its high compute and me…
IVE: An Accelerator for Single-Server Private Information Retrieval Using Versatile Processing Elements
Sangpyo Kim, Hyesung Ji, Jongmin Kim +3
Private information retrieval (PIR) is an essential cryptographic protocol for privacy-preserving applications, enabling a client to retrieve a record from a server's database with…
Cheddar: A Swift Fully Homomorphic Encryption Library Designed for GPU Architectures
Wonseok Choi, Jongmin Kim, Jung Ho Ahn
Fully homomorphic encryption (FHE) frees cloud computing from privacy concerns by enabling secure computation on encrypted data. However, its substantial computational and memory o…
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…
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…
ARK: Fully Homomorphic Encryption Accelerator with Runtime Data Generation and Inter-Operation Key Reuse
Jongmin Kim, Gwangho Lee, Sangpyo Kim +4
Homomorphic Encryption (HE) is one of the most promising post-quantum cryptographic schemes that enable privacy-preserving computation on servers. However, noise accumulates as we…
BTS: An Accelerator for Bootstrappable Fully Homomorphic Encryption
Sangpyo Kim, Jongmin Kim, Michael Jaemin Kim +4
Homomorphic encryption (HE) enables the secure offloading of computations to the cloud by providing computation on encrypted data (ciphertexts). HE is based on noisy encryption sch…