4 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…
Private Iris Recognition with High-Performance FHE
Jincheol Ha, Guillaume Hanrot, Taeyeong Noh +3
Among biometric verification systems, irises stand out because they offer high accuracy even in large-scale databases. For example, the World ID project aims to provide authenticat…
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…
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…