A Secure Data Publishing and Access Service for Sensitive Data from Living Labs: Enabling Collaboration with External Researchers via Shareable Data

Author:

Hernandez Mikel12ORCID,Konstantinidis Evdokimos34ORCID,Epelde Gorka25ORCID,Londoño Francisco2,Petsani Despoina3ORCID,Timoleon Michalis3ORCID,Fiska Vasiliki6ORCID,Mpaltadoros Lampros6ORCID,Maga-Nteve Christoniki6,Machairas Ilias3,Bamidis Panagiotis D.3ORCID

Affiliation:

1. Computer Science and Artificial Intelligence Department, Computer Science Faculty, University of the Basque Country (UPV/EHU), 20018 Donostia-San Sebastian, Spain

2. Digital Health and Biomedical Technologies, Vicomtech Foundation, Basque Research and Technology Alliance (BRTA), 20009 Donostia-San Sebastian, Spain

3. Laboratory of Medical Physics and Digital Innovation, School of Medicine, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece

4. European Network of Living Labs, 1210 Brussels, Belgium

5. eHealth Group, Biogipuzkoa Health Research Institute, 20014 Donostia-San Sebastian, Spain

6. Centre for Research & Technology Hellas, Information Technologies Institute (ITI), 57001 Thermi-Thessaloniki, Greece

Abstract

Intending to enable a broader collaboration with the scientific community while maintaining privacy of the data stored and generated in Living Labs, this paper presents the Shareable Data Publishing and Access Service for Living Labs, implemented within the framework of the H2020 VITALISE project. Building upon previous work, significant enhancements and improvements are presented in the architecture enabling Living Labs to securely publish collected data in an internal and isolated node for external use. External researchers can access a portal to discover and download shareable data versions (anonymised or synthetic data) derived from the data stored across different Living Labs that they can use to develop, test, and debug their processing scripts locally, adhering to legal and ethical data handling practices. Subsequently, they may request remote execution of the same algorithms against the real internal data in Living Lab nodes, comparing the outcomes with those obtained using shareable data. The paper details the architecture, data flows, technical details and validation of the service with real-world usage examples, demonstrating its efficacy in promoting data-driven research in digital health while preserving privacy. The presented service can be used as an intermediary between Living Labs and external researchers for secure data exchange and to accelerate research on data analytics paradigms in digital health, ensuring compliance with data protection laws.

Funder

Horizon 2020 Framework Program of the European Union for Research Innovation

Publisher

MDPI AG

Reference42 articles.

1. (2023, June 20). General Data Protection Regulation (GDPR)—Official Legal Text. Available online: https://gdpr-info.eu/.

2. (2023, August 09). VITALISE Project—Home. Available online: https://vitalise-project.eu/.

3. (2023, August 09). VITALISE Project—Why VITALISE. Available online: https://vitalise-project.eu/why-vitalise/.

4. Standardized and Extensible Reference Data Model for Clinical Research in Living Labs;Epelde;Procedia Comput. Sci.,2022

5. Hernandez, M., Epelde, G., Beristain, A., Álvarez, R., Molina, C., Larrea, X., Alberdi, A., Timoleon, M., Bamidis, P., and Konstantinidis, E. (2022). Incorporation of Synthetic Data Generation Techniques within a Controlled Data Processing Workflow in the Health and Wellbeing Domain. Electronics, 11.

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