180 citations · 509 across the 9 of their papers we have counts for
6 papers · 1 filter
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…
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…
Accelerating Number Theoretic Transformations for Bootstrappable Homomorphic Encryption on GPUs
Sangpyo Kim, Wonkyung Jung, Jaiyoung Park +1
Homomorphic encryption (HE) draws huge attention as it provides a way of privacy-preserving computations on encrypted messages. Number Theoretic Transform (NTT), a specialized form…