CORDIS Project
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This project addresses privacy concerns in foundation models, which are advanced machine learning systems trained on large datasets. It aims to develop methods to prevent data leakage during the model's lifecycle, ensuring individual privacy is maintained from training to deployment.
Novel foundation models (FMs) like GPT, LLaMA, and Stable Diffusion are achieving exceptional performance across diverse tasks, generating high-quality text, images, and audio, and driving industry innovations.
This progress stems from a shift in machine learning paradigm: instead of training task-specific models on curated datasets, FMs are first pretrained on vast, uncurated data to become strong general-purpose models, then adapted on smaller, domain-specific datasets for specific tasks.Howev…
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