Under CopyrightAkbari Gurabi, MehdiMehdiAkbari GurabiHermsen, FelixFelixHermsenMandal, AvikarshaAvikarshaMandalDecker, StefanStefanDecker2025-05-052025-05-052024https://doi.org/10.24406/publica-4595https://publica.fraunhofer.de/handle/publica/48723310.1007/978-3-031-57978-3_1110.24406/publica-4595The 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.enPrivacy-Preserving Machine LearningPrivacy Enhancing TechnologiesData SovereigntyData Spaces000 Informatik, Informationswissenschaft, allgemeine Werke::000 Informatik, Wissen, Systeme::004 Datenverarbeitung; Informatik000 Informatik, Informationswissenschaft, allgemeine Werke::000 Informatik, Wissen, Systeme::006 Spezielle Computerverfahrenconference paper