Scalable Automated Analysis of Access Control and Privacy Policies
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Access Control is becoming increasingly important for today ubiquitous systems. Sophisticated security requirements need to be ensured by authorization policies for increasingly complex and large applications. As a consequence, designers need to understand such policies and ensure that they meet the desired security constraints while administrators must also maintain them so as to comply with the evolving needs of systems and applications. These tasks are greatly complicated by the expressiveness and the dimensions of the authorization policies. It is thus necessary to provide policy designers and administrators with automated analysis techniques that are capable to foresee if, and under what conditions, security properties may be violated. In this paper, we consider this analysis problem in the context of the Role-Based Access Control (RBAC), one of the most widespread access control models. We describe how we design heuristics to enable an analysis tool, called asaspXL, to scale up to handle large and complex Administrative RBAC policies. We also discuss the capability of applying the techniques inside the tool to the analysis of location-based privacy policies. An extensive experimentation shows that the proposed heuristics play a key role in the success of the analysis tool over the state-of-the-art analysis tools.
This research is funded by Vietnam National University Ho Chi Minh City (VNU-HCM) under grant number C2017-20-17.
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