4 papers
Biologically-Informed Hybrid Membership Inference Attacks on Generative Genomic Models
Asia Belfiore, Jonathan Passerat-Palmbach, Dmitrii Usynin
The increased availability of genetic data has transformed genomics research, but raised many privacy concerns regarding its handling due to its sensitive nature. This work explore…
Differentially Private aggregate hints in mev-share
Jonathan Passerat-Palmbach, Sarisht Wadhwa
Flashbots recently released mev-share to empower users with control over the amount of information they share with searchers for extracting Maximal Extractable Value (MEV). Searche…
Efficient and Private: Memorisation under differentially private parameter-efficient fine-tuning in language models
Olivia Ma, Jonathan Passerat-Palmbach, Dmitrii Usynin
Fine-tuning large language models (LLMs) for specific tasks introduces privacy risks, as models may inadvertently memorise and leak sensitive training data. While Differential Priv…
Model Exploration Using OpenMOLE - a workflow engine for large scale distributed design of experiments and parameter tuning
Romain Reuillon, Mathieu Leclaire, Jonathan Passerat-Palmbach
OpenMOLE is a scientific workflow engine with a strong emphasis on workload distribution. Workflows are designed using a high level Domain Specific Language (DSL) built on top of S…