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MUSKETEER SIGNED

Machine learning to augment shared knowledge in federated privacy-preserving scenarios

Total Cost €

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EC-Contrib. €

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Partnership

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Project "MUSKETEER" data sheet

The following table provides information about the project.

Coordinator
IBM IRELAND LIMITED 

Organization address
address: IBM House, Shelbourne Road
city: Ballsbridge
postcode: Dublin 4
website: n.a.

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 Coordinator Country Ireland [IE]
 Total cost 4˙380˙346 €
 EC max contribution 4˙380˙335 € (100%)
 Programme 1. H2020-EU.2.1.1. (INDUSTRIAL LEADERSHIP - Leadership in enabling and industrial technologies - Information and Communication Technologies (ICT))
 Code Call H2020-ICT-2018-2
 Funding Scheme RIA
 Starting year 2018
 Duration (year-month-day) from 2018-12-01   to  2021-11-30

 Partnership

Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    IBM IRELAND LIMITED IE (Ballsbridge) coordinator 830˙900.00
2    IMPERIAL COLLEGE OF SCIENCE TECHNOLOGY AND MEDICINE UK (LONDON) participant 675˙185.00
3    INTERNATIONAL DATA SPACES EV DE (BERLIN) participant 551˙250.00
4    TREE TECHNOLOGY SA ES (SIERO) participant 462˙500.00
5    ENGINEERING - INGEGNERIA INFORMATICA SPA IT (ROMA) participant 407˙500.00
6    UNIVERSIDAD CARLOS III DE MADRID ES (GETAFE (MADRID)) participant 397˙500.00
7    BIOTRONICS 3D LIMITED UK (LONDON) participant 295˙000.00
8    KATHOLIEKE UNIVERSITEIT LEUVEN BE (LEUVEN) participant 260˙500.00
9    FCA INFORMATION TECHNOLOGY, EXCELLENCE AND METHODS SPA IT (TORINO) participant 217˙500.00
10    COMAU SPA IT (GRUGLIASCO) participant 155˙000.00
11    DIAGNOSTIKON KAI THERAPEFTIKON KENTRON ATHINON YGEIA ANONYMOS ETAIREIA EL (MAROUSI) participant 127˙500.00

Map

 Project objective

The massive increase in data collected and stored worldwide calls for new ways to preserve privacy while still allowing data sharing among multiple data owners. Today, the lack of trusted and secure environments for data sharing inhibits data economy while legality, privacy, trustworthiness, data value and confidentiality hamper the free flow of data. By the end of the project, MUSKETEER aims to create a validated, federated, privacy-preserving machine learning platform tested on industrial data that is inter-operable, scalable and efficient enough to be deployed in real use cases. MUSKETEER aims to alleviate data sharing barriers by providing secure, scalable and privacy-preserving analytics over decentralized datasets using machine learning. Data can continue to be stored in different locations with different privacy constraints, but shared securely. The MUSKETEER cross-domain platform will validate progress in the industrial scenarios of smart manufacturing and health. MUSKETEER strives to (1) create machine learning models over a variety of privacy-preserving scenarios, (2) ensure security and robustness against external and internal threats, (3) provide a standardized and extendable architecture, (4) demonstrate and validate in two different industrial scenarios and (5) enhance data economy by boosting sharing across domains. The MUSKETEER impact crosses industrial, scientific, economic and strategic domains. Real-world industry requirements and outcomes are validated in an operational setting. Federated machine learning approaches for data sharing are innovated. Data economy is fostered by creating a rewarding model capable of fairly monetizing datasets according to the real data value. Finally, Europe is positioned as a leader in innovative data sharing technologies.

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The information about "MUSKETEER" are provided by the European Opendata Portal: CORDIS opendata.

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