Introducing the TRUMPET project: TRUstworthy Multi-site Privacy Enhancing Technologies
Alberto Pedrouzo-Ulloa, Jan Ramon, Fernando Péerez-González, Siyanna Lilova, Patrick Duflot, Zakaria Chihani, Nicola Gentili, Paola Ulivi, Mohammad Ashadul Hoque, Twaha Mukammel, Zeev Pritzker, Augustin Lemesle, Jaime Loureiro-Acuña, Xavier Martínez, Gonzalo Jiménez-Balsa · 2023
This paper is an overview of the EU-funded project TRUMPET (https://trumpetproject.eu/), and gives an outline of its scope and main technical aspects and objectives. In recent years, Federated Learning has emerged as a revolutionary privacy-enhancing technology. However, further research has cast a shadow of doubt on its strength for privacy protection. The goal of TRUMPET is to research and develop novel privacy enhancement methods for Federated Learning, and to deliver a highly scalable Federated AI service platform for researchers, that will enable AI-powered studies of siloed, multi-site, cross-domain, cross-border European datasets with privacy guarantees that follow the requirements of GDPR. The generic TRUMPET platform will be piloted, demonstrated and validated in the specific use case of European cancer hospitals, allowing researchers and policymakers to extract AI-driven insights from previously inaccessible cross-border, cross-organization cancer data, while ensuring the patients' privacy.