  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.
