Saudi Arabia is in the process of implementing Electronic Patient Records (EPR) throughout its National Health services. One of the key challenges during the adoption process is the security of EPR. This thesis investigates the current state of EPR security in Saudi Arabia’s National Health Services (SA NHS) both from a policy perspective and with regard to its implementation in SA NHS’s information systems. To facilitate the analysis of EPR security, an EPR model has been developed that captures the information that is stored as part of the electronic record system in conjunction with stated security requirements. This model is used in the analysis of policy consistency and to validate operational reality against stated policies at various levels within the SA NHS. The model is based on a comprehensive literature survey and structured interviews which established the current state of practice with respect to EPRs in a representative Saudi Arabian hospital. The key contribution of this research is the development and evaluation of a structured and model-based analysis approach to EPR security at the early adoption stage in SA, based on types of information present in EPRs and the needs of the users of EPRs. The key findings show that the SA EPR adoption process is currently proceeding without serious consideration for security policy to protect EPR and a lack of awareness amongst hospital staff.

The purpose of this study is to propose a top-down policy engineering framework for attribute-based access control (ABAC) that aims to automatically extract ACPs from requirement specifications documents, and then, using the extracted policies, build or update an ABAC model. We specify a procedure that consists of three main components: 1) ACP sentence identification, 2) policy element extraction, and 3) ABAC model creation and update. ACP sentence identification processes unrestricted natural language documents and identify the sentences that carry ACP content. We propose and compare three different methodologies from different disciplines, namely deep recurrent neural networks (RNN-based), biological immune system (BIS-based), and a combination of multiple natural language processing techniques (PMI-based) in order to identify the proper methodology for extracting ACP sentences from irrelevant text. Our evaluation results improve the state-of-the-art by a margin of 5% F1-Measure. To aid future research, we also introduce a new dataset that includes 5000 sentences from real-world policy documents. ABAC policy extraction extracts ACP elements such as subject, object, and action from the identified ACPs. We use semantic roles and correctly identify ACP elements with an average F1 score of 75%, which bests the previous work by 15%. Furthermore, as SRL tools are often trained on publicly available corpora such as Wall Street Journal, we investigate the idea of improving SRL performance using domain-related knowledge. We utilize domain adaptation and semi-supervised learning techniques and improve the SRL performance by 2% using only a small amount of access control data. The third component, ABAC model creation and update, builds a new ABAC model or updates an existing one using the extracted ACP elements. For this purpose, we present an efficient methodology based on a particle swarm optimization algorithm for solving ABAC policy mining with minimal perturbation. Experimental results demonstrate that the proposed methodology generates much less complex policies than previous works using the same realistic case studies. Furthermore, we perform experiments on how to find an ABAC state as similar as possible to both the existing state and the optimal state. Part of the data utilized in this study was collected from the University of North Texas Policy Office, as well as policy documents from the university of North Texas Health Science Center, for the school years 2015-2016 through 2016-2017.

abstract: Access control is one of the most fundamental security mechanisms used in the design and management of modern information systems. However, there still exists an open question on how formal access control models can be automatically analyzed and fully realized in secure system development. Furthermore, specifying and managing access control policies are often error-prone due to the lack of effective analysis mechanisms and tools. In this dissertation, I present an Assurance Management Framework (AMF) that is designed to cope with various assurance management requirements from both access control system development and policy-based computing. On one hand, the AMF framework facilitates comprehensive analysis and thorough realization of formal access control models in secure system development. I demonstrate how this method can be applied to build role-based access control systems by adopting the NIST/ANSI RBAC standard as an underlying security model. On the other hand, the AMF framework ensures the correctness of access control policies in policy-based computing through automated reasoning techniques and anomaly management mechanisms. A systematic method is presented to formulate XACML in Answer Set Programming (ASP) that allows users to leverage off-the-shelf ASP solvers for a variety of analysis services. In addition, I introduce a novel anomaly management mechanism, along with a grid-based visualization approach, which enables systematic and effective detection and resolution of policy anomalies. I further evaluate the AMF framework through modeling and analyzing multiparty access control in Online Social Networks (OSNs). A MultiParty Access Control (MPAC) model is formulated to capture the essence of multiparty authorization requirements in OSNs. In particular, I show how AMF can be applied to OSNs for identifying and resolving privacy conflicts, and representing and reasoning about MPAC model and policy. To demonstrate the feasibility of the proposed methodology, a suite of proof-of-concept prototype systems is implemented as well. / Dissertation/Thesis / Ph.D. Computer Science 2012

No description available.

We consider the distributed access enforcement problem for Role-Based Access Control (RBAC) systems. Such enforcement has become important with RBAC's increasing adoption, and the proliferation of data that needs to be protected. We provide a platform for assessing candidates for access enforcement in a distributed architecture for enforcement. The platform provides the ability to encode data structures and algorithms for enforcement, and to measure time-, space- and administrative efficiency. To validate our platform, we use it to compare the state of the art in enforcement, CPOL [6], with two other approaches, the directed graph and the access matrix [9, 10]. We consider encodings of RBAC sessions in each, and propose and justify a benchmark for the assessment. We conclude with the somewhat surprising observation that CPOL is not necessarily the most efficient approach for access enforcement in distributed RBAC deployments.

隨著無線網路環境的普及，越來越多行動工作者可以透過隨身的手持設備進行網路漫遊，即時地存取不同的服務。另外亦可以經由公司的虛擬專有網路來存取企業內部資料、電子郵件及其它應用程式。不論是針對企業或是網路服務業者而言，為了要能夠確保網路環境上的安全性，眾多的行動工作者在進行漫遊時，身分必須被驗證，進而才能被授予各項服務的存取權。此外，還必需根據使用者服務使用的情況進行計費，來提升服務提供者的收益。 因此在無線區域網路中，結合一套認證、授權、計費的架構(Authentication, Authorization, Accounting Architecture, AAA Architecture)，使得網路服務業者能夠有效地來管理龐大行動工作者的跨網路漫遊服務。 本研究提出一個以情境知覺運算(context-aware computing)為基礎的AAA架構。以情境來設計資源存取政策，因此系統偵測到行動工作者情境上的改變，根據已定義好的存取政策，動態地進行身分驗證及調整授權服務，最後再依不同的服務使用等級、連線時間與網路使用量等來計費。本論文以漫遊服務與企業虛擬專有網路為例，說明在此架構下如何針對不同的情境進行身分認證、與服務授權。 / With the popularity of the Wireless LAN, mobile workers are able to access various services or resources with seamless roaming, as well as mobile VPN, just via their handheld devices. Not only for the corporations but the Internet Service Providers(ISP), a secure and trusted remote access is required. User identity should be authenticated in advance, and the service providers grant or deny mobile users the access to resources according to their statuses. Besides, a usage-based accounting and billing is crucial to provide commercialized services within WLAN, and to benefit those service providers. As a result, a AAA architecture designed for coordinating the authentication, authorization and accounting between different administrative networks is required with urgent need. The objective of this research is to provide a context-aware based AAA architecture which adopts context as the design principle to define access control policies. So the system can detect the changing contexts of mobile workers, re-authenticate user identity, adjust dynamically service permissions in the light of context-based access control policies, and bill the user taking into account the contexts efficiently. In this research, we take examples of roaming services and VPN to describe how the architecture works.

We consider the distributed access enforcement problem for Role-Based Access Control (RBAC) systems. Such enforcement has become important with RBAC's increasing adoption, and the proliferation of data that needs to be protected. We provide a platform for assessing candidates for access enforcement in a distributed architecture for enforcement. The platform provides the ability to encode data structures and algorithms for enforcement, and to measure time-, space- and administrative efficiency. To validate our platform, we use it to compare the state of the art in enforcement, CPOL [6], with two other approaches, the directed graph and the access matrix [9, 10]. We consider encodings of RBAC sessions in each, and propose and justify a benchmark for the assessment. We conclude with the somewhat surprising observation that CPOL is not necessarily the most efficient approach for access enforcement in distributed RBAC deployments.