Enabling Attribute-based Access Control in NoSQL Databases.

NoSQL databases are being increasingly used for efficient management of high volumes of unstructured data in applications like information retrieval, natural language processing, social computing, etc. However, unlike traditional databases, data protection measures such as access control for these databases are still in their infancy, which could lead to significant vulnerabilities and security/privacy issues as their adoption increases. Attribute-based Access Control (ABAC), which provides a flexible and dynamic solution to access control, can be effective for mediating accesses in typical usage scenarios for NoSQL databases. In this paper, we propose a novel methodology for enabling ABAC in NoSQL databases. Specifically we consider MongoDB, which is one of the most popular NoSQL databases in use today. We present an approach to both specify ABAC access control policies and to enforce them when an actual access request has been made. MongoDB Wire Protocol is used for extracting and processing appropriate information from the requests. We also present a method for supporting dynamic access decisions using environmental attributes and handling of ad-hoc access requests through digitally signed user attributes. Results from an extensive set of experiments on the Enron corpus as well as on synthetically generated data demonstrate the scalability of our approach. Finally, we provide details of our implementation on MongoDB and share a Github repository so that any organization can download and deploy the same for enabling ABAC in their own MongoDB installations.

Contemporaneous Update and Enforcement of ABAC Policies.

Access control policies are dynamic in nature, and therefore require frequent updates to synchronize with the latest organizational security requirements. As these updates are handled, it is important that all user access requests be answered contemporaneously and correctly without any interruption or delay. In this paper, considering the context of Attribute Based Access Control (ABAC), we propose an approach that is capable of immediately materializing any update to the policy and ensuring that it is taken into account for any subsequent access requests. One possibility is to update the policy based on the incoming changes through ABAC policy mining techniques. However, it turns out that no existing mining approach can offer correct enforcement of policies when access requests are entertained during the updates. We provide a formal proof for this surprising result and then propose an approach called δwOP that does not suffer from this problem. Essentially, δwOP keeps track of the needed information from updates and uses this in conjunction with the existing ABAC policy rules to make access decisions. We present the complexity analysis as well as a comprehensive experimental evaluation to demonstrate the efficacy of the proposed approach for different types of changes.

Enabling Attribute-Based Access Control in Linux Kernel.

Linux has built-in security features based on discretionary access control that can be enhanced using the Linux Security Module (LSM) framework. However, so far there has been no reported work on strengthening Linux with Attribute-Based Access Control (ABAC), which is gaining in popularity in recent years due to its flexibility and dynamic nature. In this paper, a method for enabling ABAC for Linux file system objects using LSM is proposed. We report initial experimental results and also share our public repository links for integrating ABAC in any Linux installation.

Enabling Workforce Optimization in Constrained Attribute-Based Access Control Systems.

Effective utilization of human capital is one of the key requirements for any successful business endeavor, with reorganization necessary if there are nonproductive employees or employees that are retiring. However, while reorganizing tasks for newer employees, it should be ensured that the employees have the requisite capabilities of handling the assigned tasks. Furthermore, security constraints forbid any arbitrary assignment of tasks to employees and also enforce major dependencies on other employees who have access to the same tasks. Since Attribute Based Access Control (ABAC) is poised to emerge as the de facto model for specifying access control policies in commercial information systems, we consider organizational policies and constraints to be modeled with ABAC. Given the increasing size and scale of organizations, both in terms of employees and resources that need to be managed, it is crucial that computational solutions are developed to automate the process of employee to task assignment. In this work, we define the Employee Replacement Problem (ERP) which answers the question of whether a given set of employees can be replaced by a smaller set of employees, while ensuring that the desired security constraints are not violated. We prove that the problem is NP-hard and use CNF-SAT to obtain a solution. An extensive experimental evaluation is carried out on diverse data sets to validate the efficiency of the proposed solution.

Incremental Maintenance of ABAC Policies.

Discovery of Attribute Based Access Control policies through mining has been studied extensively in the literature. However, current solutions assume that the rules are to be mined from a static data set of access permissions and that this process only needs to be done once. However, in real life, access policies are dynamic in nature and may change based on the situation. Simply utilizing the current approaches would necessitate that the mining algorithm be re-executed for every update in the permissions or user/object attributes, which would be significantly inefficient. In this paper, we propose to incrementally maintain ABAC policies by only updating the rules that may be affected due to any change in the underlying access permissions or attributes. A comprehensive experimental evaluation demonstrates that the proposed incremental approach is significantly more efficient than the conventional ABAC mining.

Optimal Employee Recruitment in Organizations under Attribute-Based Access Control.

For any successful business endeavor, recruitment of required number of appropriately qualified employees in proper positions is a key requirement. For effective utilization of human resources, reorganization of such workforce assignment is also a task of utmost importance. This includes situations when the under-performing employees have to be substituted with fresh applicants. Generally, the number of candidates applying for a position is large and hence, the task of identifying an optimal subset becomes critical. Moreover, a human resource manager would also like to make use of the opportunity of retirement of employees to improve manpower utilization. However, the constraints enforced by the security policies prohibit any arbitrary assignment of tasks to employees. Further, the new employees should have the capabilities required to handle the assigned tasks. In this article, we formalize this problem as the Optimal Recruitment Problem (ORP), wherein the goal is to select the minimum number of fresh employees from a set of candidates to fill the vacant positions created by the outgoing employees, while ensuring satisfiability of the specified security conditions. The model used for specification of authorization policies and constraints is Attribute Based Access Control (ABAC), since it is considered to be the de facto next generation framework for handling organizational security policies. We show that the ORP problem is NP-hard and propose a greedy heuristic for solving it. Extensive experimental evaluation shows both the effectiveness as well as efficiency of the proposed solution.

Algorithms and Applications to Weighted Rank-one Binary Matrix Factorization.

Many applications use data that are better represented in the binary matrix form, such as click-stream data, market basket data, document-term data, user-permission data in access control, and others. Matrix factorization methods have been widely used tools for the analysis of high-dimensional data, as they automatically extract sparse and meaningful features from data vectors. However, existing matrix factorization methods do not work well for the binary data. One crucial limitation is interpretability, as many matrix factorization methods decompose an input matrix into matrices with fractional or even negative components, which are hard to interpret in many real settings. Some matrix factorization methods, like binary matrix factorization, do limit decomposed matrices to binary values. However, these models are not flexible to accommodate some data analysis tasks, like trading off summary size with quality and discriminating different types of approximation errors. To address those issues, this article presents weighted rank-one binary matrix factorization, which is to approximate a binary matrix by the product of two binary vectors, with parameters controlling different types of approximation errors. By systematically running weighted rank-one binary matrix factorization, one can effectively perform various binary data analysis tasks, like compression, clustering, and pattern discovery. Theoretical properties on weighted rank-one binary matrix factorization are investigated and its connection to problems in other research domains are examined. As weighted rank-one binary matrix factorization in general is NP-hard, efficient and effective algorithms are presented. Extensive studies on applications of weighted rank-one binary matrix factorization are also conducted.

Policy Adaptation in Hierarchical Attribute-Based Access Control Systems.

In Attribute-Based Access Control (ABAC), access to resources is given based on the attributes of subjects, objects, and environment. There is an imminent need for the development of efficient algorithms that enable migration to ABAC. However, existing policy mining approaches do not consider possible adaptation to the policy of a similar organization. In this article, we address the problem of automatically determining an optimal assignment of attribute values to subjects for enabling the desired accesses to be granted while minimizing the number of ABAC rules used by each subject or other appropriate metrics. We show the problem to be NP-Complete and propose a heuristic solution.

Managing Attribute-Based Access Control Policies in a Unified Framework using Data Warehousing and In-Memory Database.

Over the last few years, various types of access control models have been proposed for expressing the growing needs of organizations. Out of these, there is an increasing interest towards specification and enforcement of flexible and dynamic decision making security policies using Attribute Based Access Control (ABAC). However, it is not easy to migrate an existing security policy specified in a different model into ABAC. Furthermore, there exists no comprehensive approach that can specify, enforce and manage ABAC policies along with other policies potentially already existing in the organization as a unified security policy. In this article, we present a unique and flexible solution that enables concurrent specification and enforcement of such security policies through storing and querying data in a multi-dimensional and multi-granular data model. Specifically, we present a unified database schema, similar to that traditionally used in data warehouse design, that can represent different types of access control policies and store relevant policies as in-memory data, thereby significantly reducing the execution time of access request evaluation. We also present a novel approach for combining multiple access control policies through meta-policies. For ease of management, an administrative schema is presented that can specify different types of administrative policies. Extensive experiments on a wide range of data sets demonstrate the viability of the proposed approach.

Security Analysis of ABAC under an Administrative Model.

In the present day computing environment, where access control decisions are often dependent on contextual information like the location of the requesting user and the time of access request, Attribute Based Access Control (ABAC) has emerged as a suitable choice for expressing security policies. In an ABAC system, access decisions depend on the set of attribute values associated with the subjects, resources and the environment in which an access request is made. In such systems, the task of managing the set of attributes associated with the entities as well as that of analyzing and understanding the security implications of each attribute assignment is of paramount importance. In this paper, we first introduce a comprehensive attribute based administrative model, named as AMABAC (Administrative Model for ABAC), for ABAC systems and then suggest a methodology for analyzing the security properties of ABAC in the presence of the administrative model. For performing analysis, we use µZ, a SMT (Satisfiability Modulo Theories) based model checking tool. We study the impact of the various components of ABAC and AMABAC on the time taken for security analysis.

Deploying ABAC policies using RBAC Systems.

The flexibility, portability and identity-less access control features of Attribute Based Access Control(ABAC) make it an attractive choice to be employed in many application domains. However, commercially viable methods for implementation of ABAC do not exist while a vast majority of organizations use Role Based Access Control (RBAC) or their temporal extensions, such as Temporal Role Based Access Control (TRBAC). In this paper, we present a solution for organizations having a RBAC/TRBAC that can deploy an ABAC policy. Essentially, we propose a method for the translation of an ABAC policy (including time constraints) into a form that can be adopted by an RBAC/TRBAC system. We experimentally demonstrate that time taken to evaluate an access request in RBAC and TRBAC systems is significantly less than that of the corresponding ABAC system. Since the cost of security management is more expensive under RBAC when compared to ABAC, we present an analysis of the different management costs and present mitigation approaches by considering various administrative operations.

HyPE: A Hybrid Approach toward Policy Engineering in Attribute-Based Access Control.

Successful deployment of attribute-based access control requires the process of policy engineering which involves constructing a set of appropriate rules, known as a policy. Policy engineering is performed either by a top-down approach that may ignore some of the existing accesses in the organization or a bottom-up approach that may form rules which are not relevant to the organizational processes. In this work, we propose a hybrid approach toward policy engineering that addresses the limitations of the top-down and the bottom-up approaches while preserving their individual advantages.

Enabling the Deployment of ABAC Policies in RBAC Systems.

The flexibility, portability and identity-less access control features of Attribute Based Access Control (ABAC) make it an attractive choice to be employed in many application domains. However, commercially viable methods for implementation of ABAC do not exist while a vast majority of organizations use Role Based Access Control (RBAC) systems. In this paper, we present a way in which organizations having a RBAC system can deploy an ABAC policy. Thus, we propose a method for the translation of an ABAC policy into a form that can be adopted by an RBAC system. We compare the cost of enforcement in ABAC and RBAC with respect to time taken to evaluate an access request, and experimentally demonstrate that RBAC is significantly better in this respect. Since the cost of security management is more expensive under RBAC when compared to ABAC, we present an analysis of the different management costs and present mitigation approaches by considering various administrative operations.

Poster: Using Gini Impurity to Mine Attribute-based Access Control Policies with Environment Attributes.

In Attribute-based Access Control (ABAC) systems, utilizing environment attributes along with the subject and object attributes introduces a dynamic nature to the access decisions. The inclusion of environment attributes helps in achieving a more fine-grained access control. In this paper, we present an ABAC policy mining algorithm that considers the environment attributes and their associated values while forming the rules. Furthermore, we use gini impurity to form the rules. This helps to minimize the number of rules in the generated policy. The experimental evaluation shows that our approach is quite effective in practice.

Efficient bottom-up Mining of Attribute Based Access Control Policies.

Attribute Based Access Control (ABAC) is fast replacing traditional access control models due to its dynamic nature, flexibility and scalability. ABAC is often used in collaborative environments. However, a major hurdle to deploying ABAC is to precisely configure the ABAC policy. In this paper, we present an ABAC mining approach that can automatically discover the appropriate ABAC policy rules. We first show that the ABAC mining problem is equivalent to identifying a set of functional dependencies in relational databases that cover all of the records in a table. We also propose a more efficient algorithm, called ABAC-SRM which discovers the most general policy rules from a set of candidate rules. We experimentally show that ABAC-SRM is accurate and significantly more efficient than the existing state of the art.