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arXiv 1111.5767cs.CRcs.LO
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PTaCL:开放系统中基于属性的访问控制语言

PTaCL: A Language for Attribute-Based Access Control in Open Systems

Jason Crampton, Charles Morisset

摘要:

近年来,许多语言和代数被提出来用于授权策略的规范。对于一些提案,如XACML,主要动机是解决现实世界的需求,通常是通过提供一种复杂的策略语言和一些非正式的评估方法;其他人试图提供更大程度的正式性(特别是在策略评估方面),但支持的功能要少得多。简而言之,很少有提案将丰富的语言功能集与定义良好的语义相结合,更少的是在开放环境中基于属性的访问控制的授权策略。在本文中,我们分解成两个不同的子语言的政策规范的问题:政策目标语言(PTL)的目标规范,它决定了一个政策时,应评估;和政策组合语言(PCL),用于建立更复杂的政策,从现有的。我们定义了两个这样的语言的语法和语义,并证明它们可以是简单的和富有表现力的。PTaCL是通过结合这两种子语言的功能而获得的语言,支持广泛的策略规范。然而,PTaCL的强大功能意味着可以定义可能产生意外结果的策略。我们提供了一个分析PTL应如何限制,以及如何写在PCL的政策应进行评估,以尽量减少不良结果的可能性。

英文摘要:

Many languages and algebras have been proposed in recent years for the specification of authorization policies. For some proposals, such as XACML, the main motivation is to address real-world requirements, typically by providing a complex policy language with somewhat informal evaluation methods; others try to provide a greater degree of formality (particularly with respect to policy evaluation) but support far fewer features. In short, there are very few proposals that combine a rich set of language features with a well-defined semantics, and even fewer that do this for authorization policies for attribute-based access control in open environments. In this paper, we decompose the problem of policy specification into two distinct sub-languages: the policy target language (PTL) for target specification, which determines when a policy should be evaluated; and the policy composition language (PCL) for building more complex policies from existing ones. We define syntax and semantics for two such languages and demonstrate that they can be both simple and expressive. PTaCL, the language obtained by combining the features of these two sub-languages, supports the specification of a wide range of policies. However, the power of PTaCL means that it is possible to define policies that could produce unexpected results. We provide an analysis of how PTL should be restricted and how policies written in PCL should be evaluated to minimize the likelihood of undesirable results.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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