Growing privacy regulations and internal governance mandates are driving demand for fine-grained, context-sensitive access control in data management systems. Among competing approaches, content-based access control -- where access decisions depend on the data values referenced by a query -- is becoming particularly prominent, and is supported directly in modern database engines. While simple content-based predicates often incur negligible overhead, increasingly rich policies can interact in subtle ways with query optimization, leading to significant and poorly understood performance variability. This paper investigates this gap by introducing a structural framework and expressive policy grammar for modelling content-based compliance policies and analysing their impact on query planning and execution in database systems. Building on this framework, we augment an analytical benchmark with structured policy workloads, enabling controlled evaluation of enforcement mechanisms and optimization strategies under combined query - policy workloads. Our experimental results show that policy structure has a decisive impact on optimizer behaviour and end-to-end performance, underscoring the need for policy-aware database and optimizer design.

翻译：日益增长的隐私法规和内部治理要求正在推动数据管理系统对细粒度、上下文敏感的访问控制的需求。在众多方法中，基于内容的访问控制（即访问决策取决于查询所引用的数据值）正变得尤为突出，并已得到现代数据库引擎的直接支持。虽然简单的基于内容谓词通常只会带来可忽略的开销，但日益复杂的策略可能会以微妙的方式与查询优化交互，导致显著且难以理解的性能波动。本文通过引入一个结构性框架和富有表现力的策略语法，用于对基于内容的合规性策略进行建模，并分析它们对数据库系统中查询规划与执行的影响，从而研究了这一空白。在此框架的基础上，我们用一个结构化的策略工作负载增强了分析基准，使得能够在联合查询-策略工作负载下对实施机制和优化策略进行受控评估。我们的实验结果表明，策略结构对优化器的行为和端到端性能具有决定性影响，凸显了需要设计策略感知的数据库和优化器。