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Towards Privacy-Preserving Machine Learning in Sovereign Data Spaces: Opportunities and Challenges

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Privacy and Identity Management. Sharing in a Digital World (Privacy and Identity 2023)

Part of the book series: IFIP Advances in Information and Communication Technology ((IFIPAICT,volume 695))

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Abstract

The world of big data has unlocked novel avenues for organizations to generate value via sharing data. Current data ecosystem initiatives such as Gaia-X and IDS are introducing data-driven business models that facilitate access to diverse data sources and automate data exchange processes among organizations. However, this also poses challenges for organizations and their customers in preserving control over their own data. This paper provides an overview of the extension requirements on current usage control concepts in data spaces through technical means to augment data privacy guarantees. Our analysis clarifies the deficiencies regarding privacy within the realms of data sovereignty and sovereign data spaces, as well as the risks and opportunities associated with the application of machine learning on sensitive data. This work identifies promising foundational elements and presents areas of research for the integration of privacy-enhancing technologies into usage control for remote data science.

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Acknowledgements

This work was funded by the TANGO project and partly supported by the BMBF-ANR-funded project Crypto4Graph-AI (funding number 01IS21100A). TANGO project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 101070052.

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Authors and Affiliations

  1. RWTH Aachen University, Aachen, Germany

    Mehdi Akbari Gurabi, Felix Hermsen & Stefan Decker

  2. Fraunhofer FIT, Sankt Augustin, Germany

    Mehdi Akbari Gurabi, Felix Hermsen, Avikarsha Mandal & Stefan Decker

Authors
  1. Mehdi Akbari Gurabi
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  2. Felix Hermsen
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  3. Avikarsha Mandal
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  4. Stefan Decker
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Corresponding author

Correspondence to Mehdi Akbari Gurabi .

Editor information

Editors and Affiliations

  1. ULD, Germany, Kiel, Germany

    Felix Bieker

  2. Vrije Universiteit Amsterdam, Amsterdam, The Netherlands

    Silvia de Conca

  3. University of Oslo, Oslo, Norway

    Nils Gruschka

  4. Karlstad University, Karlstad, Sweden

    Meiko Jensen

  5. Ostfalia University of Applied Sciences, Wolfenbüttel, Niedersachsen, Germany

    Ina Schiering

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Akbari Gurabi, M., Hermsen, F., Mandal, A., Decker, S. (2024). Towards Privacy-Preserving Machine Learning in Sovereign Data Spaces: Opportunities and Challenges. In: Bieker, F., de Conca, S., Gruschka, N., Jensen, M., Schiering, I. (eds) Privacy and Identity Management. Sharing in a Digital World. Privacy and Identity 2023. IFIP Advances in Information and Communication Technology, vol 695. Springer, Cham. https://doi.org/10.1007/978-3-031-57978-3_11

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  • DOI: https://doi.org/10.1007/978-3-031-57978-3_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-57977-6

  • Online ISBN: 978-3-031-57978-3

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