2 citations · 3 across the 3 of their papers we have counts for
3 papers
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…
Deep Neural Networks for Encrypted Inference with TFHE
Andrei Stoian, Jordan Frery, Roman Bredehoft +3
Fully homomorphic encryption (FHE) is an encryption method that allows to perform computation on encrypted data, without decryption. FHE preserves the privacy of the users of onlin…