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Scaling Up secure Processing, Anonymization and generation of Health Data for EU cross border collaborative research and Innovation

Project description

DEENESFRITPL

A healthy dose of big data processing

Big data is data that contains huge and hard-to-manage volumes of structured and unstructured data. It is so big that it is difficult or impossible to process using traditional methods. The EU-funded SECURED project will increase efficiency by scaling up multi-party computation, data anonymisation and synthetic data generation. Focusing on private and unbiased AI and data analytics, it will demonstrate technologies developed in health-related use cases like real-time tumour classification, telemonitoring for children and access to genomic data. In addition to speeding up and facilitating privacy preserving data-driven tools and services for well-being, prevention, diagnosis, treatment and follow-up care, SECURED will also analyse the current ethical and legal challenges to data sharing.

Objective

The overall goal of the SECURED project is to scale up multiparty computation, data anonymization and synthetic data generation, by increasing efficiency and improving security, with a focus on private and unbiased artificial intelligence and data analytics, health-related data and data hubs, and cross-border cooperation. The project will address the limitations that are currently preventing the widespread use of secure multiparty computation and effective anonymization, namely: the limited practical capabilities of current cryptographic schemes for secure multi-party computation protocols, and their performance; the lack of well understood and standardized data anonymization methods for health data; the absence of dynamic and on demand services for generating synthetic data; the complex and ad-hoc nature of current federation protocols for machine learning and AI-based data analytics; the lack of support for health technology providers to implement privacy enhancing technologies, in particular SMEs.
SECURED will tackle these challenges by focusing on scaling up privacy technologies via algorithmic improvements and implementation efficiency (HW and SW), as well as the generalization of primitives and definitions, with the aim of speeding up and facilitating privacy preserving data-driven tools and services for wellbeing, prevention, diagnosis, treatment and follow-up care. SECURED will also analyse the current ethical and legal challenges to data sharing, and is targeted at overcoming current limited adoption of advanced multi-party computation and data anonymization technologies by providing direct support to health technology SMEs through a funding call. To ensure relevance to real-world settings, SECURED will showcase the technologies developed in four health-related use cases provided by partner hospitals and health stakeholders, namely: real-time tumor classification; telemonitoring for children; synthetic data generation for education; access to genomic data.

Fields of science

Keywords

Programme(s)

Topic(s)

Call for proposal

HORIZON-HLTH-2022-IND-13

See other projects for this call

Funding Scheme

RIA - Research and Innovation action

Coordinator

UNIVERSITEIT VAN AMSTERDAM
Net EU contribution
€ 752 418,75
Address
Spui 21
1012WX Amsterdam
Netherlands

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Region
West-Nederland Noord-Holland Groot-Amsterdam
Activity type
Higher or Secondary Education Establishments
Links
Other funding
€ 0,00

Participants (17)

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EC signature date 23 November 2022
Last update: 28 November 2022

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Permalink: https://cordis.europa.eu/project/id/101095717

European Union, 2023