Quantitative access control with partially-observable Markov decision processes

This paper presents a novel access control framework reducing the access control problem to a traditional decision problem, thus allowing a policy designer to reuse tools and techniques from the decision theory. We propose here to express, within a single framework, the notion of utility of an access, decisions beyond the traditional allowing/denying of an access, the uncertainty over the effect of executing a given decision, the uncertainty over the current state of the system, and to optimize this process for a (probabilistic) sequence of requests. We show that an access control mechanism including these different concepts can be specified as a (Partially Observable) Markov Decision Process, and we illustrate this framework with a running example, which includes notions of conflict, critical resource, mitigation and auditing decisions, and we show that for a given sequence of requests, it is possible to calculate an optimal policy different from the naive one. This optimization is still possible even for several probable sequences of requests.