Role In Mining Research Articles

Over-Fitting and Error Detection for Online Role Mining

Recent research has attempted to use role-based approaches to recommend mobile services to other members among the same group in a context dependent manner. However, the traditional role mining approaches originated from the domain of security control tend to be rigid and may not be able to capture human behaviors adequately. In particular, during the course of role mining process, these approaches easily result in over-fitting, i.e., too many roles with slightly different service consumption patterns are found. As a result, they fail to reveal the true common preferences within the user community. This paper proposes an online role mining algorithm with a residual term and an error term, that automatically group users according to their interests and habits without losing sight of their individual preferences and random errors. Moreover, to resolve the over-fitting problem, the authors relax the role definition in role mining mechanism by introducing quasi-roles based on the concept of quasi-bicliques. Most importantly, the new concept allows us to propose a monitoring framework to detect and correct over-fitting in online role mining such that recommendations can be made based on the latest and genuine common preferences. To the best of the authors’ knowledge, this is a new area in service recommendation that is yet to be fully explored.

Visual Role Mining: A Picture Is Worth a Thousand Roles

This paper offers a new role engineering approach to Role-Based Access Control (RBAC), referred to as visual role mining. The key idea is to graphically represent user-permission assignments to enable quick analysis and elicitation of meaningful roles. First, we formally define the problem by introducing a metric for the quality of the visualization. Then, we prove that finding the best representation according to the defined metric is a NP-hard problem. In turn, we propose two algorithms: ADVISER and EXTRACT. The former is a heuristic used to best represent the user-permission assignments of a given set of roles. The latter is a fast probabilistic algorithm that, when used in conjunction with ADVISER, allows for a visual elicitation of roles even in absence of predefined roles. Besides being rooted in sound theory, our proposal is supported by extensive simulations run over real data. Results confirm the quality of the proposal and demonstrate its viability in supporting role engineering decisions.

Permission and role automatic assigning of user in role-based access control

Role mining and setup affect the usage of role-based access control (RBAC). Traditionally, user’s role and permission assigning are manipulated by security administrator of system. However, the cost is expensive and the operating process is complex. A new role analyzing method was proposed by generating mappings and using them to provide recommendation for systems. The relation among sets of permissions, roles and users was explored by generating mappings, and the relation between sets of users and attributes was analyzed by means of the concept lattice model, generating a critical mapping between the attribute and permission sets, and making the meaning of the role natural and operational. Thus, a role is determined by permission set and user’s attributes. The generated mappings were used to automatically assign permissions and roles to new users. Experimental results show that the proposed algorithm is effective and efficient.

A business-driven decomposition methodology for role mining

It is generally accepted that role mining – that is, the discovery of roles through the automatic analysis of data from existing access control systems – must count on business requirements to increase its effectiveness. Indeed, roles elicited without leveraging on business information are unlikely to be intelligible by system administrators. A business-oriented categorization of users and permissions (e.g., organizational units, job titles, cost centers, business processes, etc.) could help administrators identify the job profiles of users and, as a consequence, which roles should be assigned to them. Nonetheless, most of the existing role mining techniques yield roles that have no clear relationship with the business structure of the organization where the role mining is being applied. To face this problem, we propose a methodology that allows role engineers to leverage business information during the role finding process. The key idea is decomposing the dataset to analyze into several partitions, in a way that each partition is homogeneous from a business perspective. Each partition groups users or permissions with the same business categorization (e.g., all the users belonging to the same department, or all the permissions that support the execution of the same business process). Such partitions are then role-mined independently, hence achieving three main results: (1) elicited roles have a clearer relationship with business information; (2) mining algorithms do not seek to find commonalities among users with fundamentally different job profiles or among uncorrelated permissions; and, (3) any role mining algorithm can be used in conjunction with our approach. When several business attributes are available, analysts need to figure out which one produces the decomposition that leads to the most intelligible roles. In this paper, we describe three indexes that drive the decomposition process by measuring the quality of a given decomposition: entrustability, minability gain, and similarity gain. We compare these indexes, pointing out pros and cons. Finally, we apply our methodology on real enterprise data, showing its effectiveness and efficiency in supporting role engineering.

Constraint-Aware Role Mining Via Extended Boolean Matrix Decomposition

The role mining problem has received considerable attention recently. Among the many solutions proposed, the Boolean matrix decomposition (BMD) formulation has stood out, which essentially discovers roles by decomposing the binary matrix representing user-to-permission assignment (UPA) into two matrices-user-to-role assignment (UA) and permission-to-role assignment (PA). However, supporting certain embedded constraints, such as separation of duty (SoD) and exceptions, is critical to the role mining process. Otherwise, the mined roles may not capture the inherent constraints of the access control policies of the organization. None of the previously proposed role mining solutions, including BMD, take into account these underlying constraints while mining. In this paper, we extend the BMD so that it reflects such embedded constraints by proposing to allow negative permissions in roles or negative role assignments for users. Specifically, by allowing negative permissions in roles, we are often able to use less roles to reconstruct the same given user-permission assignments. Moreover, from the resultant roles we can discover underlying constraints such as separation of duty constraints. This feature is not supported by any existing role mining approaches. Hence, we call the role mining problem with negative authorizations the constraint-aware role mining problem (CRM). We also explore other interesting variants of the CRM, which may occur in real situations. To enable CRM and its variants, we propose a novel approach, extended Boolean matrix decomposition (EBMD), which addresses the ineffectiveness of BMD in its ability of capturing underlying constraints. We analyze the computational complexity for each of CRM variants and present heuristics for problems that are proven to be NP-hard.

Multi-assignment clustering for boolean data

We propose a probabilistic model for clustering Boolean data where an object can be simultaneously assigned to multiple clusters. By explicitly modeling the underlying generative process that combines the individual source emissions, highly structured data are expressed with substantially fewer clusters compared to single-assignment clustering. As a consequence, such a model provides robust parameter estimators even when the number of samples is low. We extend the model with different noise processes and demonstrate that maximum-likelihood estimation with multiple assignments consistently infers source parameters more accurately than single-assignment clustering. Our model is primarily motivated by the task of role mining for role-based access control, where users of a system are assigned one or more roles. In experiments with real-world access-control data, our model exhibits better generalization performance than state-of-the-art approaches.

Mining Roles with Multiple Objectives

With the growing adoption of Role-Based Access Control (RBAC) in commercial security and identity management products, how to facilitate the process of migrating a non-RBAC system to an RBAC system has become a problem with significant business impact. Researchers have proposed to use data mining techniques to discover roles to complement the costly top-down approaches for RBAC system construction. An important problem is how to construct RBAC systems with low complexity. In this article, we define the notion of weighted structural complexity measure and propose a role mining algorithm that mines RBAC systems with low structural complexity. Another key problem that has not been adequately addressed by existing role mining approaches is how to discover roles with semantic meanings. In this article, we study the problem in two primary settings with different information availability. When the only information is user-permission relation, we propose to discover roles whose semantic meaning is based on formal concept lattices. We argue that the theory of formal concept analysis provides a solid theoretical foundation for mining roles from a user-permission relation. When user-attribute information is also available, we propose to create roles that can be explained by expressions of user-attributes. Since an expression of attributes describes a real-world concept, the corresponding role represents a real-world concept as well. Furthermore, the algorithms we propose balance the semantic guarantee of roles with system complexity. Finally, we indicate how to create a hybrid approach combining top-down candidate roles. Our experimental results demonstrate the effectiveness of our approaches.

A new role mining framework to elicit business roles and to mitigate enterprise risk

Role-based access control (RBAC) allows to effectively manage the risk derived from granting access to resources, provided that designed roles are business-driven. Role mining represents an essential tool for role engineers, but existing techniques are not able to elicit roles with an associated clear business meaning. Hence, it is difficult to mitigate risk, to simplify business governance, and to ensure compliance throughout the enterprise. To elicit meaningful roles, we propose a methodology where data to analyze are decomposed into smaller subsets according to the provided business information. We introduce two indices, minability and similarity, that drive the decomposition process by providing the expected complexity to find roles with business meaning. The proposed methodology is rooted on a sound theoretical framework. Moreover, experiments on real enterprise data support its effectiveness.

Role Engineering via Prioritized Subset Enumeration

Today, role-based access control (RBAC) has become a well-accepted paradigm for implementing access control because of its convenience and ease of administration. However, in order to realize the full benefits of the RBAC paradigm, one must first define the roles accurately. This task of defining roles and associating permissions with them, also known as role engineering, is typically accomplished either in a top-down or in a bottom-up manner. Under the top-down approach, a careful analysis of the business processes is done to first define job functions and then to specify appropriate roles from them. While this approach can help in defining roles more accurately, it is tedious and time consuming since it requires that the semantics of the business processes be well understood. Moreover, it ignores existing permissions within an organization and does not utilize them. On the other hand, under the bottom-up approach, existing permissions are used to derive roles from them. As a result, it may help automate the process of role definition. In this paper, we present an unsupervised approach, called RoleMiner, for mining roles from existing user-permission assignments. Since a role, when semantics are unavailable, is nothing but a set of permissions, the task of role mining is essentially that of clustering users having the same (or similar) permissions. However, unlike the traditional applications of data mining that ideally require identification of nonoverlapping clusters, roles will have overlapping permissions and thus permission sets that define roles should be allowed to overlap. It is this distinction from traditional clustering that makes the problem of role mining nontrivial. Our experiments with real and simulated data sets indicate that our role mining process is quite accurate and efficient. Since our role mining approach is based on subset enumeration, it is fairly robust to reasonable levels of noise.

The role mining problem

Devising a complete and correct set of roles has been recognized as one of the most important and challenging tasks in implementing role-based access control. A key problem related to this is the notion of goodness/interestingness—when is a role good/interesting? In this article, we define the Role Mining Problem (RMP) as the problem of discovering an optimal set of roles from existing user permissions. The main contribution of this article is to formally define RMP and analyze its theoretical bounds. In addition to the above basic RMP, we introduce two different variations of the RMP, called the δ-Approx RMP and the minimal-noise RMP that have pragmatic implications. We reduce the known “Set Basis Problem” to RMP to show that RMP is an NP-complete problem. An important contribution of this article is also to show the relation of the RMP to several problems already identified in the data mining and data analysis literature. By showing that the RMP is in essence reducible to these known problems, we can directly borrow the existing implementation solutions and guide further research in this direction. We also develop a heuristic solution based on the previously proposed FastMiner algorithm, which is very accurate and efficient.

The role mining problem

The role mining problem

Transaction Mining for Fraud Detection in ERP Systems

Despite all attempts to prevent fraud, it continues to be a major threat to industry and government. Traditionally, organizations have focused on fraud prevention rather than detection, to combat fraud. In this paper we present a role mining inspired approach to represent user behaviour in Enterprise Resource Planning (ERP) systems, primarily aimed at detecting opportunities to commit fraud or potentially suspicious activities. We have adapted an approach which uses set theory to create transaction profiles based on analysis of user activity records. Based on these transaction profiles, we propose a set of (1) anomaly types to detect potentially suspicious user behaviour, and (2) scenarios to identify inadequate segregation of duties in an ERP environment. In addition, we present two algorithms to construct a directed acyclic graph to represent relationships between transaction profiles. Experiments were conducted using a real dataset obtained from a teaching environment and a demonstration dataset, both using SAP R/3, presently the predominant ERP system. The results of this empirical research demonstrate the effectiveness of the proposed approach.

Minimal role mining method for Web service composition

Web service composition is a low cost and efficient way to leverage the existing resource and implementation. In current Web service composition implementations, the issue of how to define the role for a new composite Web service has been little addressed. Adjusting the access control policy for a new composite Web service always causes substantial administration overhead from the security administrator. Furthermore, the distributed nature of Web service based applications makes traditional role mining methods obsolete. In this paper, we analyze the minimal role mining problem for Web service composition, and prove that this problem is NP-complete. We propose a sub-optimal greedy algorithm based on the analysis of necessary role mapping for interoperation across multiple domains. Simulation shows the effectiveness of our algorithm, and compared to the existing methods, our algorithm has significant performance advantages. We also demonstrate the practical application of our method in a real agent based Web service system. The results show that our method could find the minimal role mapping efficiently.

Taming role mining complexity in RBAC

In this paper we address the problem of reducing the role mining complexity in RBAC systems. To this aim, we propose a three steps methodology: first, we associate a weight to roles; second, we identify user-permission assignments that cannot belong to roles with a weight exceeding a given threshold; and third, we restrict the role-finding problem to user-permission assignments identified in the second step. We formally show—the proofs of our results are rooted in graph theory—that this methodology allows role engineers for the elicitation of stable candidate roles, by contextually simplifying the role selection task. Efficient algorithms to implement our strategy are also described. Further, we discuss practical applications of our approach. Finally, we tested our methodology on real dataset. Results achieved confirm both the viability of our proposal and the analytical findings.

I.U.G.S. Committee on storage, automatic processing and retrieval of geological data

Mining is often portrayed as a contributor to sustainable development, especially so in developing countries such as Papua New Guinea (PNG). Since 1970, several large mines have been developed in PNG (e.g. Panguna, Ok Tedi, Porgera, Lihir, Ramu) but always with controversial environmental standards and social impacts often overlooked or ignored. In PNG, mine wastes are approved to be discharged to rivers or oceans on a very large scale, leading to widespread environmental and social impacts – to the point of civil war in the case of Panguna. The intimate links between indigenous communities and their environment have invariably been under-estimated or ignored, leading many to question mining's role in PNG's development. Here, we review the geology of PNG, its mineral resources, mining history, key trends for grades and resources, environmental metrics (water, energy, carbon), mine waste management, and regulatory and governance issues. The study provides a unique and comprehensive insight into the sustainable development contribution of the mining industry in PNG – especially the controversial practices of riverine and marine mine waste disposal. The history of mining is a complex story of the links between the anthroposphere, biosphere, hydrosphere and geosphere. Ultimately, this study demonstrates that the scale of environmental and social impacts and risks are clearly related to the vast scale of mine wastes – a fact which remains been poorly recognised. For PNG, the promise of mining-led development remains elusive to many communities and they are invariably left with significant social and environmental legacies which will last for decades to centuries (e.g. mine waste impacts on water resources). Most recently, the PNG government has moved to ban riverine tailings disposal for future projects and encourage greater transparency and accountability by the mining sector, including its interactions with communities. There remains hope for better outcomes in the future.