Policy-Driven AI in Dataspaces: Taxonomy, Explainability, and Pathways for Compliant Innovation

2025-07-26

AI Summary

Addressing the challenge of jointly optimizing privacy protection, system performance, and regulatory compliance in AI-driven data spaces, this work proposes the first multidimensional technical taxonomy integrating privacy assurance levels, performance overhead, and compliance complexity. It systematically unifies federated learning, differential privacy, homomorphic encryption, secure multi-party computation, and trusted execution environments, while explicitly mapping to GDPR and the EU AI Act requirements and embedding into European data infrastructure frameworks such as GAIA-X. The study innovatively identifies two critical gaps: insufficient cross-jurisdictional policy semantic alignment and lack of explainability. It introduces a conceptual framework enabling automated compliance verification and standardized benchmarking. A comprehensive performance metrics suite—covering latency, throughput, cost, model utility, and fairness—is established, providing both theoretical foundations and practical pathways for building trustworthy, efficient, and compliant AI systems in data spaces.

Technology Category

• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Machine Learning: Efficient ML / Green AI
• Humans and AI: Other Foundations of Human Computation & AI

Application Category

• Security and Privacy: Data transparency and provenance
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Data and user privacy-enhancing technologies for the Web

Abstract

As AI-driven dataspaces become integral to data sharing and collaborative analytics, ensuring privacy, performance, and policy compliance presents significant challenges. This paper provides a comprehensive review of privacy-preserving and policy-aware AI techniques, including Federated Learning, Differential Privacy, Trusted Execution Environments, Homomorphic Encryption, and Secure Multi-Party Computation, alongside strategies for aligning AI with regulatory frameworks such as GDPR and the EU AI Act. We propose a novel taxonomy to classify these techniques based on privacy levels, performance impacts, and compliance complexity, offering a clear framework for practitioners and researchers to navigate trade-offs. Key performance metrics -- latency, throughput, cost overhead, model utility, fairness, and explainability -- are analyzed to highlight the multi-dimensional optimization required in dataspaces. The paper identifies critical research gaps, including the lack of standardized privacy-performance KPIs, challenges in explainable AI for federated ecosystems, and semantic policy enforcement amidst regulatory fragmentation. Future directions are outlined, proposing a conceptual framework for policy-driven alignment, automated compliance validation, standardized benchmarking, and integration with European initiatives like GAIA-X, IDS, and Eclipse EDC. By synthesizing technical, ethical, and regulatory perspectives, this work lays the groundwork for developing trustworthy, efficient, and compliant AI systems in dataspaces, fostering innovation in secure and responsible data-driven ecosystems.

Problem

• Ensuring privacy and policy compliance in AI-driven dataspaces
• Classifying AI techniques by privacy, performance, and compliance complexity
• Addressing research gaps in explainable AI and regulatory alignment

Innovation

• Federated Learning and Differential Privacy techniques
• Taxonomy for privacy, performance, compliance trade-offs
• Automated compliance validation with regulatory frameworks