68 citations · 112 across the 8 of their papers we have counts for
8 papers
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…
Fast Homomorphic Linear Algebra with BLAS
Youngjin Bae, Jung Hee Cheon, Guillaume Hanrot +2
Homomorphic encryption is a cryptographic paradigm allowing to compute on encrypted data, opening a wide range of applications in privacy-preserving data manipulation, notably in A…
SIMD-Aware Homomorphic Compression and Application to Private Database Query
Jung Hee Cheon, Keewoo Lee, Jai Hyun Park +1
In a private database query scheme (PDQ), a server maintains a database, and users send queries to retrieve records of interest from the server while keeping their queries private.…
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…
Privacy-Preserving Text Classification on BERT Embeddings with Homomorphic Encryption
Garam Lee, Minsoo Kim, Jai Hyun Park +2
Embeddings, which compress information in raw text into semantics-preserving low-dimensional vectors, have been widely adopted for their efficacy. However, recent research has show…