20 citations · 20 across the 6 of their papers we have counts for
4 papers · 1 filter
Scaling up FHE-based Privacy-Preserving ML: Higher Throughput, Longer Inputs for LLama-3-8B
Jaiyoung Park, Sejin Park, Jai Hyun Park +6
As large language models (LLMs) become ubiquitous, privacy concerns pertaining to inference keep growing. Fully homomorphic encryption (FHE) has emerged as a primary cryptographic…
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…
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…