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Using access control policy rules with deny effects (i.e., negative authorization) can be preferred to using complemented conditions in the rules as they are often easier to comprehend in the context of large policies. However, the two constructs have different impacts on the expressiveness of a rule-based access control model. We investigate whether policies expressible using complemented conditions can be expressed using deny rules instead. The answer to this question is not always affirmative. In this paper, we propose a practical approach to address this problem for a given policy. In particular, we develop theoretical results that allow us to pose the problem as a set of queries to an SAT solver. Our experimental results using an off-the-shelf SAT solver demonstrate the feasibility of our approach and offer insights into its performance based on access control policies from multiple domains. 

Obtaining an accurate specification of the access control policy enforced by an application is essential in ensuring that it meets our security/privacy expectations. This is especially important as many of real-world applications handle a large amount and variety of data objects that may have different applicable policies. We investigate the problem of automated learning of access control policies from web applications. The existing research on mining access control policies has mainly focused on developing algorithms for inferring correct and concise policies from low-level authorization information. However, little has been done in terms of systematically gathering the low-level authorization data and applications' data models that are prerequisite to such a mining process. In this paper, we propose a novel black-box approach to inferring those prerequisites and discuss our initial observations on employing such a framework in learning policies from real-world web applications.