Project description
Privacy preservation throughout foundational model training
A novel shift in the machine learning paradigm, specifically pretraining foundational models (FMs) on uncurated data followed by domain-specific datasets for specific tasks, has led to advancements for FMs such as LLaMA, GPT, and stable diffusion, enabling them to drive industry innovations. Unfortunately, FMs leak information from their training data, leading to a serious privacy risk whenever they utilise private data. In this context, the ERC-funded Privacy4FMs project will develop innovative methods for privacy preservation from pre-training to deployment and throughout the development of FMs. The project will research and pinpoint the sources of leaks, audit FMs to detect privacy violations, and develop a novel theoretical framework for privacy guarantees in FMs.
Objective
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.
However, FMs leak information from their training data. For example, recent studies reveal that they can re-create individual data points from their pretraining and adaptation datasets. This poses serious privacy risks when private data is involved. Preventing exposure requires developing methods to ensure privacy-preservation throughout FMs' lifecycle, from pretraining to deployment. To achieve this, our project will identify sources of privacy leakage, provide privacy guarantees over both pretraining and adaptation, and audit FMs to detect privacy violations. Therefore, we must overcome three major challenges: the limited understanding of privacy risks in FM pretraining, the lack of formal joint privacy guarantees for pretraining and adaptation, and the ineffectiveness of current privacy auditing methods.
The solution that we propose will establish a novel theoretical framework for privacy guarantees in FMs under the pretrain-adapt paradigm. Our fundamental innovations rely on the insight that, due to complex interdependencies between pretraining and adaptation data, different data points require individual levels of protection to prevent leakage. Advancing methods for identifying, achieving, and accounting for such individual guarantees will enable us to formally bound privacy leakage over both training stages and to detect violations. These innovations will allow society to benefit from technological advancements through FMs without compromising individuals' privacy.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
This project's classification has been human-validated.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
This project's classification has been human-validated.
Keywords
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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HORIZON.1.1 - European Research Council (ERC)
MAIN PROGRAMME
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Topic(s)
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Funding Scheme
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
HORIZON-ERC - HORIZON ERC Grants
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Call for proposal
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) ERC-2025-STG
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Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.
66123 SAARBRUCKEN
Germany
The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.