cs.AI updates on arXiv.org 07月29日 12:22
Policy-Driven AI in Dataspaces: Taxonomy, Explainability, and Pathways for Compliant Innovation
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本文综述了数据空间中隐私保护与政策感知的AI技术,包括联邦学习、差分隐私等,并提出了一种基于隐私、性能和合规复杂度的分类法,以指导AI系统在数据空间中的应用。

arXiv:2507.20014v1 Announce Type: cross 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.

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数据空间 AI隐私保护 联邦学习 差分隐私 合规性
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