4 citations · 7 across the 4 of their papers we have counts for
3 papers · 1 filter
Private LoRA Fine-tuning of Open-Source LLMs with Homomorphic Encryption
Jordan Frery, Roman Bredehoft, Jakub Klemsa +2
Preserving data confidentiality during the fine-tuning of open-source Large Language Models (LLMs) is crucial for sensitive applications. This work introduces an interactive protoc…
Neural Network Training on Encrypted Data with TFHE
Luis Montero, Jordan Frery, Celia Kherfallah +2
We present an approach to outsourcing of training neural networks while preserving data confidentiality from malicious parties. We use fully homomorphic encryption to build a unifi…
Privacy-Preserving Tree-Based Inference with TFHE
Jordan Frery, Andrei Stoian, Roman Bredehoft +4
Privacy enhancing technologies (PETs) have been proposed as a way to protect the privacy of data while still allowing for data analysis. In this work, we focus on Fully Homomorphic…