Thinking in terms of causality helps us structure how different parts of a system depend on each other, and how interventions on one part of a system may result in changes to other parts. Therefore, formal models of causality are an attractive tool for reasoning about security, which concerns itself with safeguarding properties of a system against interventions that may be malicious. As we show, many security properties are naturally expressed as nested causal statements: not only do we consider what caused a particular undesirable effect, but we also consider what caused this causal relationship itself to hold. We present a natural way to extend the Halpern-Pearl (HP) framework for causality to capture such nested causal statements. This extension adds expressivity, enabling the HP framework to distinguish between causal scenarios that it could not previously naturally tell apart. We moreover revisit some design decisions of the HP framework that were made with non-nested causal statements in mind, such as the choice to treat specific values of causal variables as opposed to the variables themselves as causes, and may no longer be appropriate for nested ones.
翻译:从因果关系的角度来思考,有助于我们构建系统中不同部分如何相互依存,以及系统某一部分的干预如何可能导致其他部分的改变。因此,正式的因果关系模型是安全推理的一个吸引人的工具,它涉及保护一个系统的特性,防止可能恶意的干预。正如我们所显示的那样,许多安全属性自然以嵌入因果说明的形式表达:我们不仅考虑什么造成了某种特别的不良效应,我们还考虑是什么造成了这种因果关系本身。我们提出了一个自然的方法来扩展Halpern-Pearl(HP)框架的因果关系框架,以捕捉这种嵌入因果说明。这一扩展增加了直观性,使HP框架能够区分以前无法自然区分的因果假设。我们还要重新审视HP框架的一些设计决定,这些决定是用非自发因果说明来作出的,例如选择将因果变数的具体值与变数本身作为原因处理,而可能不再适合于宿主。