arXiv:2607.15987cs.AI2026-07

首个具备严格数学语义的ODRL策略评估工具,解决数据访问权限一致性难题。

A Formally Grounded ODRL Evaluator: Implementation and Comparison

  • 基于形式化语义构建静态与流式场景下的策略评估算法
  • 支持所有规则类型,性能测试覆盖策略复杂度与数据规模影响
  • 为欧盟数据空间提供可互操作、结果一致的AI治理解决方案

ODRL策略语言正成为欧洲数据空间中数据访问、使用偏好及AI治理政策的事实标准。当前标准缺乏数学形式语义,导致各系统自行解释语言,限制了互操作性且无法保证结果一致性。本文基于已有ODRL语义模型,形式化定义了访问控制与监控场景下的策略评估问题,涵盖静态与流式设置,并提出一种新颖高效的算法与实现。我们构建了首个具备透明形式语义的ODRL评估器,支持全部规则类型。通过实验测量其性能,分析了策略复杂度与被评估数据规模对可扩展性的影响。我们还对比了现有主流评估器,揭示了在支持功能与评估模式上的关键差异。

原文摘要 · Abstract (English)

The ODRL policy language is emerging as the de-facto standard for policy modelling data access and usage preferences, AI governance policies and data workflows in European dataspaces. The current standard has no mathematical formal semantics to describe how a system should implement policy evaluation. This has resulted in a variety of systems and tools that implement their own interpretation of the language, which limits interoperability and cannot guarantee consistent results. Based on an existing semantic model of ODRL, we formalise the problems of ODRL evaluation for the access control and monitoring scenarios, in both static and streaming settings, and we provide a novel, efficient algorithm and implementation. We present the first ODRL Evaluator with transparent formal semantics and supporting all rule types. We experimentally measure its performance, analysing different scalability dimensions related to policy complexity and size of the data on which a policy is evaluated. We compare our system with the state-of-the-art by providing a comparative review of existing ODRL evaluators, which highlights the differences in supported ODRL features and evaluation modes.

策略评估ODRLAI治理形式化

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