This paper proposes a computational model for policy administration. As an organization evolves, new users and resources are gradually placed under the mediation of the access control model. Each time such new entities are added, the policy administrator must deliberate on how the access control policy shall be revised to reflect the new reality. A well-designed access control model must anticipate such changes so that the administration cost does not become prohibitive when the organization scales up. Unfortunately, past Access Control research does not offer a formal way to quantify the cost of policy administration. In this work, we propose to model ongoing policy administration in an active learning framework. Administration cost can be quantified in terms of query complexity. We demonstrate the utility of this approach by applying it to the evolution of protection domains. We also modelled different policy administration strategies in our framework. This allowed us to formally demonstrate that domain-based policies have a cost advantage over access control matrices because of the use of heuristic reasoning when the policy evolves. To the best of our knowledge, this is the first work to employ an active learning framework to study the cost of policy deliberation and demonstrate the cost advantage of heuristic policy administration.

翻译：本文提出了一种用于策略管理的计算模型。随着组织的发展，新用户和资源逐渐被纳入访问控制模型的管理范围。每当新增此类实体时，策略管理员必须权衡如何修改访问控制策略以反映新的现实情况。一个设计良好的访问控制模型必须预见到此类变化，以免在组织规模扩大时管理成本变得过高。遗憾的是，以往的访问控制研究并未提供正式的方法来量化策略管理成本。在本研究中，我们提出在主动学习框架下对持续的策略管理进行建模。管理成本可以用查询复杂度来量化。通过将其应用于保护域的演进，我们证明了该方法的实用性。我们还在此框架中对不同的策略管理策略进行了建模，从而得以正式证明：由于策略演进过程中采用了启发式推理，基于域的策略相比访问控制矩阵具有成本优势。据我们所知，这是首个运用主动学习框架研究策略权衡成本，并证明启发式策略管理成本优势的研究工作。