Introducing the TRUMPET project: TRUstworthy Multi-site Privacy Enhancing Technologies

A. Pedrouzo-Ulloa, J. Ramon, Fernando Péerez-González, Siyanna Lilova, Patrick Duflot, Zakaria Chihani, N. Gentili, P. Ulivi, Mohammad Ashadul Hoque, Twaha Mukammel, Zeev Pritzker, Augustin Lemesle, J. Loureiro-Acuña, Xavier Martínez, G. Jiménez-Balsa
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Abstract

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.
介绍TRUMPET项目:可信赖的多站点隐私增强技术
本文概述了欧盟资助的项目TRUMPET (https://trumpetproject.eu/),并概述了其范围、主要技术方面和目标。近年来,联邦学习已经成为一种革命性的隐私增强技术。然而,进一步的研究对其隐私保护的力度投下了怀疑的阴影。TRUMPET的目标是为联邦学习研究和开发新的隐私增强方法,并为研究人员提供一个高度可扩展的联邦人工智能服务平台,这将使人工智能研究能够对孤立的、多站点的、跨域的、跨境的欧洲数据集进行研究,并保证隐私符合GDPR的要求。通用的TRUMPET平台将在欧洲癌症医院的具体用例中进行试点、演示和验证,使研究人员和政策制定者能够从以前无法访问的跨国界、跨组织癌症数据中提取人工智能驱动的见解,同时确保患者的隐私。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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