100 citations · 112 across the 4 of their papers we have counts for
3 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…
Fast and Accurate Homomorphic Softmax Evaluation
Wonhee Cho, Guillaume Hanrot, Taeseong Kim +2
Homomorphic encryption is one of the main solutions for building secure and privacy-preserving solutions for Machine Learning as a Service. This motivates the development of homomo…
Encryption-Friendly LLM Architecture
Donghwan Rho, Taeseong Kim, Minje Park +4
Large language models (LLMs) offer personalized responses based on user interactions, but this use case raises serious privacy concerns. Homomorphic encryption (HE) is a cryptograp…