A Trust Management Framework for Decision Support Systems

In the era of information explosion, it is critical to develop a framework which can extract useful information and help people to make “educated” decisions. In our lives, whether we are aware of it, trust has turned out to be very helpful for us to make decisions. At the same time, cognitive trust, especially in large systems, such as Facebook, Twitter, and so on, needs support from computer systems. Therefore, we need a framework that can effectively, but also intuitively, let people express their trust, and enable the system to automatically and securely summarize the massive amounts of trust information, so that a user of the system can make “educated” decisions, or at least not blind decisions. Inspired by the similarities between human trust and physical measurements, this dissertation proposes a measurement theory based trust management framework. It consists of three phases: trust modeling, trust inference, and decision making. Instead of proposing specific trust inference formulas, this dissertation proposes a fundamental framework which is flexible and can be adapted by many different inference formulas. Validation experiments are done on two data sets: the Epinions.com data set and the Twitter data set. This dissertation also adapts the measurement theory based trust management framework for two decision support applications. In the first application, the real stock market data is used as ground truth for the measurement theory based trust management framework. Basically, the correlation between the sentiment expressed on Twitter and stock market data is measured. Compared with existing works which do not differentiate tweets’ authors, this dissertation analyzes trust among stock investors on Twitter and uses the trust network to differentiate tweets’ authors. The results show that by using the measurement theory based trust framework, Twitter sentiment valence is able to reflect abnormal stock returns better than treating all the authors as equally important or weighting them by their number of followers. In the second application, the measurement theory based trust management framework is used to help to detect and prevent from being attacked in cloud computing scenarios. In this application, each single flow is treated as a measurement. The simulation results show that the measurement theory based trust management framework is able to provide guidance for cloud administrators and customers to make decisions, e.g. migrating tasks from suspect nodes to trustworthy nodes, dynamically allocating resources according to trust information, and managing the trade-off between the degree of redundancy and the cost of resources

Consumer side resource accounting in cloud computing

PhD ThesisCloud computing services made available to consumers range from providing basic computational resources such as storage and compute power to sophisticated enterprise application services. A common business model is to charge consumers on a pay-per-use basis where they periodically pay for the resources they have consumed. The provider is responsible for measuring and collecting the resource usage data. This approach is termed provider-side accounting. A serious limitation of this approach is that consumers have no choice but to take whatever usage data that is made available by the provider as trustworthy. This thesis investigates whether it is possible to perform consumer-side resource accounting where a consumer independently collects, for a given cloud service, all the data required for calculating billing charges. If this were possible, then consumers will be able to perform reasonableness checks on the resource usage data available from service providers as well as raise alarms when apparent discrepancies are suspected in consumption figures. Two fundamental resources of cloud computing, namely, storage and computing are evaluated. The evaluation exercise reveals that the resource accounting models of popular cloud service providers, such as Amazon, are not entirely suited to consumer-side resource accounting, in that discrepancies between the data collected by the provider and the consumer can occur. The thesis precisely identifies the causes that could lead to such discrepancies and points out how the discrepancies can be resolved. The results from the thesis can be used by service providers to improve their resource accounting models. In particular, the thesis shows how an accounting model can be made strongly consumer–centric so that all the data that the model requires for calculating billing charges can be collected independently by the consumer. Strongly consumer–centric accounting models have the desirable property of openness and transparency, since service users are in a position to verify the charges billed to them.Cultural Affairs Department, Libyan Embassy, Londo

From a Barrier to a Bridge: Data-Privacy in Deregulated Smart Grids

The introduction of so-called smart meters involves detailed consumption data. While this data plays a key role in integrating volatile renewable energy sources, a side effect is that it can reveal sensitive personal information. Concerns and protests led to a stopped smart meter rollout yet. In deregulated electricity markets, data-privacy is even more at risk: The UK, Texas and Ontario decided for a nation-wide communication intermediary in order to facilitate the exchange of the vast amount of smart meter data. However, this operational efficiency is achieved by the fact that an intermediary is a single point of failure. We present an approach based on encryption to secure the intermediary against privacy invasions and we can show that our prototypical implementation meets even restrictive requirements for large-scale data handling and processing. By aiming at customers’ confidence in smart metering, our solution might lay the ground for an ecosystem of energy services

IoT trust and reputation: a survey and taxonomy

IoT is one of the fastest-growing technologies and it is estimated that more than a billion devices would be utilized across the globe by the end of 2030. To maximize the capability of these connected entities, trust and reputation among IoT entities is essential. Several trust management models have been proposed in the IoT environment; however, these schemes have not fully addressed the IoT devices features, such as devices role, device type and its dynamic behavior in a smart environment. As a result, traditional trust and reputation models are insufficient to tackle these characteristics and uncertainty risks while connecting nodes to the network. Whilst continuous study has been carried out and various articles suggest promising solutions in constrained environments, research on trust and reputation is still at its infancy. In this paper, we carry out a comprehensive literature review on state-of-the-art research on the trust and reputation of IoT devices and systems. Specifically, we first propose a new structure, namely a new taxonomy, to organize the trust and reputation models based on the ways trust is managed. The proposed taxonomy comprises of traditional trust management-based systems and artificial intelligence-based systems, and combine both the classes which encourage the existing schemes to adapt these emerging concepts. This collaboration between the conventional mathematical and the advanced ML models result in design schemes that are more robust and efficient. Then we drill down to compare and analyse the methods and applications of these systems based on community-accepted performance metrics, e.g. scalability, delay, cooperativeness and efficiency. Finally, built upon the findings of the analysis, we identify and discuss open research issues and challenges, and further speculate and point out future research directions.Comment: 20 pages, 5 Figures, 3 tables, Journal of cloud computin

IoT trust and reputation: a survey and taxonomy

IoT is one of the fastest-growing technologies and it is estimated that more than a billion devices would be utilized across the globe by the end of 2030. To maximize the capability of these connected entities, trust and reputation among IoT entities is essential. Several trust management models have been proposed in the IoT environment; however, these schemes have not fully addressed the IoT devices features, such as devices role, device type and its dynamic behavior in a smart environment. As a result, traditional trust and reputation models are insufficient to tackle these characteristics and uncertainty risks while connecting nodes to the network. Whilst continuous study has been carried out and various articles suggest promising solutions in constrained environments, research on trust and reputation is still at its infancy. In this paper, we carry out a comprehensive literature review on state-of-the-art research on the trust and reputation of IoT devices and systems. Specifically, we first propose a new structure, namely a new taxonomy, to organize the trust and reputation models based on the ways trust is managed. The proposed taxonomy comprises of traditional trust management-based systems and artificial intelligence-based systems, and combine both the classes which encourage the existing schemes to adapt these emerging concepts. This collaboration between the conventional mathematical and the advanced ML models result in design schemes that are more robust and efficient. Then we drill down to compare and analyse the methods and applications of these systems based on community-accepted performance metrics, e.g. scalability, delay, cooperativeness and efficiency. Finally, built upon the findings of the analysis, we identify and discuss open research issues and challenges, and further speculate and point out future research directions.Comment: 20 pages, 5 Figures, 3 tables, Journal of cloud computin

Security Enhanced Applications for Information Systems

Every day, more users access services and electronically transmit information which is usually disseminated over insecure networks and processed by websites and databases, which lack proper security protection mechanisms and tools. This may have an impact on both the users’ trust as well as the reputation of the system’s stakeholders. Designing and implementing security enhanced systems is of vital importance. Therefore, this book aims to present a number of innovative security enhanced applications. It is titled “Security Enhanced Applications for Information Systems” and includes 11 chapters. This book is a quality guide for teaching purposes as well as for young researchers since it presents leading innovative contributions on security enhanced applications on various Information Systems. It involves cases based on the standalone, network and Cloud environments