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The main goal of modern access control policy languages is to offer high-level languages, by using which security officers and application developers can express a large variety of access restrictions and isolate the security logic from the application logic. However, the current state-of-the-art language, XACML, suffers from some design flaws and lacks important features, such as those that characterize the RBAC model. Therefore, we propose an access control language that combines the benefits of both XACML and RBAC while avoiding their drawbacks.

In this article, we introduce a comprehensive framework supporting a privacy-aware access control mechanism, that is, a mechanism tailored to enforce access control to data containing personally identifiable information and, as such, privacy sensitive. The key component of the framework is a family of models (P-RBAC) that extend the well-known RBAC model in order to provide full support for expressing highly complex privacy-related policies, taking into account features like purposes and obligations. We formally define the notion of privacy-aware permissions and the notion of conflicting permission assignments in P-RBAC, together with efficient conflict-checking algorithms. The framework also includes a flexible authoring tool, based on the use of the SPARCLE system, supporting the high-level specification of P-RBAC permissions. SPARCLE supports the use of natural language for authoring policies and is able to automatically generate P-RBAC permissions from these natural language specifications. In the article, we also report performance evaluation results and contrast our approach with other relevant access control and privacy policy frameworks such as P3P, EPAL, and XACML.

Fuzzy inference is a promising approach to implement risk-based access control systems. However, its application to access control raises some novel problems that have not been yet investigated. First, because there are many different fuzzy operations, one must choose the fuzzy operations that best address security requirements. Second, risk-based access control, though it improves information flow and better addresses requirements from critical organizations, may result in damages by malicious users before mitigating steps are taken. Third, the scalability of a fuzzy inference-based access control system is questionable. The time required by a fuzzy inference engine to estimate risks may be quite high especially when there are tens of parameters and hundreds of fuzzy rules. However, an access control system may need to serve hundreds or thousands of users. In this paper, we investigate these issues and present our solutions or answers to them.

In curated databases, annotations may contain opinions different from those in sources. Moreover, annotations may contradict each other and have uncertainty. Such situations result in a natural question: "Which opinion is most likely to be correct?" In this paper, we define a credibility-enhanced curated database and propose an efficient method to accurately evaluate the correctness of sources and annotations in curated databases.

This paper proposes a D-algebra to compose decisions from multiple access control policies. Compared to other algebra-based approaches aimed at policy composition, D-algebra is the only one that satisfies both functional completeness (any possible decision matrix can be expressed by a D-algebra formula) and computational effectiveness (a formula can be computed efficiently given any decision matrix). The D-algebra has several relevant applications in the context of access control policies, namely the analysis of policy languages decision mechanisms, and the development of tools for policy authoring and enforcement.

Data (or information) provenance has many important applications. However, prior work on data provenance management almost exclusively focused on the collection, representation, query, and storage of provenance data. In contrast, the security aspect of provenance management has not been understood nor adequately addressed. A natural question then is: What would a secure provenance management system - perhaps as an analogy to secure database management systems - look like? In this paper, we explore the problem space of secure provenance management systems with an emphasis on the security requirements for such systems, and characterize desired solutions for tackling the problem. We believe that this paper makes a significant step towards a comprehensive solution to the problem of secure provenance management.

In this paper, we present a novel obligation model for the Core Privacy-aware Role Based Access Control (P-RBAC), and discuss some design issues in detail. Pre-obligations, post-obligations, conditional obligations, and repeating obligations are supported by the obligation model. Interaction between permissions and obligations is discussed, and efficient algorithms are provided to detect undesired effects.

Privacy has been acknowledged to be a critical requirement for many business (and non-business) environments. Therefore, the definition of an expressive and easy-to-use privacy related access control model, based on which privacy policies can be specified, is crucial. In this work we introduce a family of models (P-RBAC) that extend the well known RBAC model in order to provide full support for expressing highly complex privacy-related policies, taking into account features like purposes and obligations. We also compare our work with access control and privacy policy frameworks such as P3P, EPAL, and XACML.