Anh Tuan Truong

Automated analysis of RBAC policies with temporal constraints and static role hierarchies

Temporal role based access control models support the specification and enforcement of several temporal constraints on role enabling, role activation, and temporal role hierarchies among others. In this paper, we define three mappings that preserve the solutions to a class of policy problems (they map security analysis problems in presence of static temporal role hierarchies to problems without them) and we show how they can be used to extend the capabilities of a tool for the analysis of administrative temporal role-based access control policies to reason in presence of temporal role hierarchies. An experimental evaluation with a prototype implementation shows the better behavior of one of the proposed mappings over the other two. To the best of our knowledge, ours is the first tool capable of reasoning with (static) temporal role hierarchies.

Boosting Model Checking to Analyse Large ARBAC Policies

The administration of access control policies is a task of paramount importance for distributed systems. A crucial analysis problem is to foresee if a set of administrators can give a user an access permission. We consider this analysis problem in the context of the Administrative Role-Based Access Control (ARBAC), one of the most widespread administrative models. Given the difficulty of taking into account the effect of all possible administrative actions, automated analysis techniques are needed. In this paper, we describe how a model checker can scale up to handle very large ARBAC policies while ensuring completeness. An extensive experimentation shows that an implementation of our techniques performs significantly better than Mohawk, a recently proposed tool that has become the reference for finding errors in ARBAC policies.

Scalable and precise automated analysis of administrative temporal role-based access control

Extensions of Role-Based Access Control (RBAC) policies taking into account contextual information (such as time and space) are increasingly being adopted in real-world applications. Their administration is complex since they must satisfy rapidly evolving needs. For this reason, automated techniques to identify unsafe sequences of administrative actions (i.e. actions generating policies by which a user can acquire permissions that may compromise some security goals) are fundamental tools in the administrators tool-kit. In this paper, we propose a precise and scalable automated analysis technique for the safety of administrative temporal RBAC policies. Our approach is to translate safety problems for this kind of policy to (decidable) reachability problems of a certain class of symbolic transition systems. The correctness of the translation allows us to design a precise analysis technique for the safety of administrative RBAC policies with a finite but unknown number of users. For scalability, we present a heuristics that allows us to reduce the set of administrative actions without losing the precision of the analysis. An extensive experimental analysis confirms the scalability and precision of the approach also in comparison with a recent analysis technique developed for the same class of temporal RBAC policies.

An Adaptive Grid-Based Approach to Location Privacy Preservation

Location privacy protection is a key factor to the development of location-based services. Location privacy relates to the protection of a user’s identity, position, and path. In a grid-based approach, the user’s position is obfuscated in a number of cells. However, this grid does not allow users to adjust the cell size which relates to a minimum privacy level. Therefore, it is hard to fix various privacy requirements from different users. This paper proposes a flexible-grid-based approach as well as an algorithm to protect the user’s location privacy. However, the user can custom conveniently his grid due to his requirement of privacy. The overlap-area problem is also counted in the algorithm. By deeply investigating on our solution, we also discuss open research issues to make the solution feasible in the practice.

Incremental Analysis of Evolving Administrative Role Based Access Control Policies

We consider the safety problem for Administrative Role-Based Access Control ARBAC policies, i.e. detecting whether sequences of administrative actions can result in policies by which a user can acquire permissions that may compromise some security goals. In particular, we are interested in sequences of safety problems generated by modifications namely, adding/deleting an element to/from the set of possible actions to an ARBAC policy accommodating the evolving needs of an organization. or resulting from fixing some safety issues. Since problems in such sequences share almost all administrative actions, we propose an incremental technique that avoids the re-computation of the solution to the current problem by re-using much of the work done on the previous problem in a sequence. An experimental evaluation shows the better performances of an implementation of our technique with respect to the only available approach to solve safety problems for evolving ARBAC policies proposed by Gofman, Luo, and Yang.

On guaranteeing k-anonymity in location databases

The development of location-based services and mobile devices has lead to an increase in the location data. Through the data mining process, some valuable information can be discovered from location data. However, the attackers may also extract some private (sensitive) information of the user and this can make threats against the user location privacy. Therefore, location privacy protection becomes a key factor to the success in privacy preserving in location-based services. In this paper, we propose a new approach as well as an algorithm to guarantee k-anonymity in a location database. The algorithm will maintain the association rules which have significance for the data mining process. Moreover, the algorithm also considers excluding new significant association rules created during the run of the algorithm.

Parameterized model checking for security policy analysis

We explain how a parameterized model checking technique can be exploited to mechanize the analysis of access control policies. The main advantage of the approach is to reason regardless of the number of users of the system in which the policy is enforced. This permits to obtain more useful results from the analysis; for instance, ensuring that sensitive permissions cannot be leaked regardless of the number of users in the system. We also briefly discuss how some heuristics make the technique scalable to handle (very) large policies. This is demonstrated by a comparative experimental evaluation with state-of-the-art tools for the analysis of access control policies.

Toward service placement on Fog computing landscape

This paper proposes an approach to optimize service placement on Fog landscape in the context of the Internet of Things (IoT). A multi-tier fog computing architecture that supports IoT service provision is devised. Based on this architecture, a novel service placement mechanism that optimizes service decentralization on Fog landscape leveraging context-aware information such as location, time, quality of services (QoS) has been proposed. An experiment has been conducted to evaluate the proposed approach with several simulations applying to smart grid applications. The results reveal the effectiveness of the proposed approach in terms of reducing latency, energy consumption, and network load in comparison with the conventional cloud computing model.

Solving the User-Role Reachability Problem in ARBAC with Role Hierarchy

Access Control is becoming increasingly important for todays ubiquitous systems since it provides mechanism to prevent sensitive resources in the systems against unauthorized users. In access control models, the administration of access control policies is an important task that raises a crucial analysis problem: if a set of administrators can give a user an unauthorized access permission. We consider the analysis problem in the context of the Administrative Role-Based Access Control (ARBAC), the most widespread administrative model. One of the main assumptions of current analysis techniques is that the role hierarchy is constant and thus can be abstracted away that results in the bad scalability of analysis techniques. In this paper, we introduce three reductions to enable an available analysis technique, namely ASASPXL, to handle the user-role reachability problem with the presence of role hierarchy. An extensive experimentation reports the superiority of our reductions in comparison with the approach used in the literature.

ASASPXL: New Clother for Analysing ARBAC Policies

Access Control is becoming increasingly important for today’s ubiquitous systems. In access control models, the administration of access control policies is an important task that raises a crucial analysis problem: if a set of administrators can give a user an unauthorized access permission. In this paper, we consider the analysis problem in the context of the Administrative Role-Based Access Control (ARBAC), one of the most widespread administrative models. We describe how we design heuristics to enable an analysis tool, called asaspXL, to scale up to handle large and complex ARBAC 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.