45 citations · 52 across the 5 of their papers we have counts for
5 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…
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…
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…
Master your Metrics with Calibration
Wissam Siblini, Jordan Fréry, Liyun He-Guelton +2
Machine learning models deployed in real-world applications are often evaluated with precision-based metrics such as F1-score or AUC-PR (Area Under the Curve of Precision Recall).…