Internet of Things (IoT) refers to a network of global and interrelated computing devices that connects humans and machines. It connects anything that has access to the internet and creates an avenue for data and information exchange. Devices within the IoT environment are embedded with processors, sensors and communication hardware that helps these devices collate data, analyze data (when needed), and transfer data amongst themselves. Even with the existence of IoT in making things easier for users and with the introduction of newer variants of IoT, several security and privacy challenges are introduced. In the rapidly evolving landscape of the Internet of Things (IoT), ensuring secure data sharing has become a critical concern. Attribute-based encryption (ABE) has emerged as a promising cryptographic technique for addressing security challenges in IoT environments. Attribute-Based Encryption (ABE) is a cryptographic method that provides public key encryption and access control based on attributes allocated. ABE can be used to encrypt data transmitted between IoT devices and the cloud. In situations where several devices have to interact with each other (e.g., smart home interacting with the user’s hospital IoT system), an intranet of things is formed, and these data is stored in the cloud. ABE can serve as a secure means of transmitting this data since these devices already possess unique attributes that can grant users access control. In this thesis, we aim to present a systematic review of the Attribute-Based Encryption techniques specifically designed for secure data sharing in IoT environments. The objective of this review is to analyze and synthesize existing research, identify trends, and highlight key findings in the field. Therefore, the document survey is chosen as the research strategy. PRISMA framework is followed in searching the eligible literature in 5 databases (Springer, ACM Digital library, Google Scholar, IEEE, Research Gate, Research Square, and Science Direct), with a final set of 30 articles retrieved from ACM Digital Library, IEEE, and Science Direct, all included for analysis. The results show insights on the several ABE approaches used in implementing a secure data sharing framework with access control (which involves enforcing policies that help data owners determine who can and cannot access their data), data privacy (which involves measures taken to ensure that confidentiality, integrity, and availability of any shared data), and data security (which involves practices that help protect any form of shared data from unauthorized access, tamper, or disclosure) in IoT devices.

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We present a general form of attribute exploration, a knowledge completion algorithm from formal concept analysis. The aim of this generalization is to extend the applicability of attribute exploration by a general description. Additionally, this may also allow for viewing different existing variants of attribute exploration as instances of a general form, as for example exploration on partial contexts.

<p dir="ltr">Appealing to individuals’ social identity is a powerful form of social influence, capable of changing the way people process information, the information they think about, and how they evaluate other people. This form of social influence can function through perceptions of normal behavior within a social group, in which members of a group interpret ambiguous information through the lens of what is considered to be normal in their ingroup. The Social Identity Decision Process hypothesis, based on Social Identity Theory and Probabilistic Persuasion Theory, suggests that group norms associated with a decider's social identity can alter the perceived importance of attributes or cues in a decision environment and the strategies that are used to make choices in situations in which the group identity is salient. Taking the U.S. political landscape as a context and examining Republican and Democrat social identities, norms from these political groups were expected to impact the attributes and strategies partisans use when choosing whether to have solar panels on a house. Two studies are reported that examined these effects through multi-attribute decision making, in which predefined decision process models assessed participant behavior to analyze which attributes best describe participants’ decision making.</p>

Advances in microprocessor and communications technologies are making it possible to deploy advanced traffic signal controllers capable of integrating emergency vehicle preemption and transit priority operations. However, investment planning for such an integrated system is not a trivial task. Investment planning for such a system requires a holistic approach that considers institutional, technical and financial issues from a systems perspective. Two distinct service providers, fire and rescue providers and transit operators, with separate operational functions, objectives, resources and constituents are involved. Performance parameters for the integrated system are not well defined and performance data are often imprecise in nature. Transportation planners and managers interested in deploying integrated emergency vehicle preemption and traffic priority systems do not have an evaluation approach or a common set of performance metrics to make an informed decision. There is a need for a simple structured analytical approach and tools to assess the impacts of an integrated emergency vehicle preemption and transit priority system as part of investment decision making processes. This need could be met with the assistance of a decision support system (DSS) developed to provide planners and managers a simple and intuitive analytical approach to assist in making investment decisions regarding emergency vehicle preemption and transit signal priority. This dissertation has two research goals: (1) to develop a decision support system framework to assess the impacts of advanced traffic signal control systems capable of integrating emergency vehicle preemption and transit signal priority operations for investment planning purposes; and (2) to develop selected analytical tools for incorporation into the decision support system framework. These analytical tools will employ fuzzy sets theory concepts, as well as cost and accident reduction factors. As part of this research, analytical tools to assess impacts on operating cost for transit and fire and rescue providers have been developed. In addition, an analytical tool was developed and employs fuzzy multi-attribute decision making methods to rank alternative transit priority strategies. These analytical tools are proposed for incorporation into the design of a decision support system in the future. / Ph. D.

In this disseration we propose semi-automated top-down policy engineering approaches for attribute-based access control (ABAC) development. Further, we propose a hybrid ABAC policy engineering approach to combine the benefits and address the shortcomings of both top-down and bottom-up approaches. In particular, we propose three frameworks: (i) ABAC attributes extraction, (ii) ABAC constraints extraction, and (iii) hybrid ABAC policy engineering. Attributes extraction framework comprises of five modules that operate together to extract attributes values from natural language access control policies (NLACPs); map the extracted values to attribute keys; and assign each key-value pair to an appropriate entity. For ABAC constraints extraction framework, we design a two-phase process to extract ABAC constraints from NLACPs. The process begins with the identification phase which focuses on identifying the right boundary of constraint expressions. Next is the normalization phase, that aims at extracting the actual elements that pose a constraint. On the other hand, our hybrid ABAC policy engineering framework consists of 5 modules. This framework combines top-down and bottom-up policy engineering techniques to overcome the shortcomings of both approaches and to generate policies that are more intuitive and relevant to actual organization policies. With this, we believe that our work takes essential steps towards a semi-automated ABAC policy development experience.