Sovereign AI - Privacy-Preserving Approaches for Managing Sensitive Public Data in Europe

This independent research report examines Sovereign AI as a governance and technical framework for artificial intelligence systems that process sensitive public sector data in Europe. The study investigates how privacy-preserving AI architectures can support data sovereignty, regulatory accountability, and public trust in contexts such as healthcare, public administration, and security. It analyzes European legal foundations including the GDPR, the Digital Services Act, and the emerging EU AI Act, and evaluates their implications for AI system design and lifecycle oversight. Technical approaches such as federated learning, homomorphic encryption, secure multi-party computation, and differential privacy are assessed for their capacity to enable collaborative AI development without exposing raw sensitive data beyond jurisdictional boundaries. The research further proposes governance structures that integrate transparency, explainability, auditability, and institutional oversight to ensure compliance with European fundamental rights and democratic control. Practical application domains illustrate how Sovereign AI principles can be operationalized in high-risk public sector environments. The report concludes with strategic directions for building European digital autonomy through privacy-preserving AI innovation and cross-border cooperation under enforceable sovereignty safeguards. This work is published as a publicly available independent study and serves as a foundation for further peer-reviewed research.