Journal of Computer Security

Administrative Role Based Access Control (ARBAC) is one of the most widespread framework for the management of access-control policies. Several automated analysis techniques have been proposed to help maintaining desirable security properties of ARBAC policies. One of the main limitation of available analysis techniques is that the set of users is bounded. In this paper, we propose a symbolic framework to overcome this limitation. We design an automated analysis technique that can handle both a bounded and an unbounded number of users by adapting recent methods for the symbolic model checking of infinite state systems that use first-order logic and SMT solving techniques. An extensive experimental evaluation confirms the scalability of the proposed technique.

We present the design, proof theory and metatheory of a logic for representing and reasoning about authorization policies. A salient feature of the logic, BL, is its support for system state in the form of interpreted predicates, upon which authorization policies often rely. In addition, BL includes Abadi et al.'s “says” connective and explicit time. BL is illustrated through a case study of policies for sharing sensitive information created in the US intelligence community. We discuss design choices in the interaction between state and other features of BL and validate BL's proof theory by proving standard metatheoretic properties like admissibility of cut.

Data sharing and dissemination are becoming increasingly important for conducting our daily life activities. The main consequence of this trend is that huge collections of data are easily available and accessible, leading to growing privacy concerns. The research community has devoted many efforts aiming at addressing the complex privacy requirements that characterize the modern Information Society. Although several advancements have been made, still many open issues need to be investigated.

In this paper, we consider a scenario where data are incrementally released and we address the privacy problem arising when sensitive non released properties depend on (and can therefore be inferred from) non-sensitive released data. We propose a model capturing this inference problem, where sensitive information is characterized by peculiar value distributions of non sensitive released data. We then describe how to counteract possible inferences that an observer can draw by applying different statistical metrics on released data. Finally, we perform an experimental evaluation of our solution, showing its efficacy.

Social networking sites have sprung up and become a hot issue of current society. In spite of the fact that these sites provide users with a variety of attractive features, much to users' dismay, however, they are prone to expose users' private information. In this paper, we propose an approach which addresses the problem of collaboratively deciding privacy policies for, but not limited to, shared photos. Our approach utilizes trust relations in social networks and combines them with Condorcet's preferential voting scheme. We study properties of our trust-augmented voting scheme and develop two approximations to improve its efficiency. Our algorithms are compared and justified by experimental results, which support the usability of our trust-augmented voting scheme.