A high-level semiotic trust agent scoring model for collaborative virtual organisations

In this paper, we describe how a semiotic ladder, together with a supportive trust agent, can be used to address “soft” trust issues in the context of collaborative Virtual Organisations (VO). The intention is to offer all parties better support for trust (as reputation) management including the reduction of risk and improved reliability of VO e-services. The semiotic ladder is intended to support the VO e-service lifecycle through the articulation of e-trust at various levels of system abstraction, including trust as measurable confidence. At the social level, reputation and reliability measures of e-trust are the relevant dimensions as regards choice of VO partner and are also relevant to the negotiation of service level agreements between the VO partners. By contrast, at the lower levels of the trust ladder, e-trust measures typically address the degree to which secure sign on and message level security conforms to various tangible technological security protocols. The novel trust agent provides the e-service consumer with an objective measure of the trustworthiness of the e-service at run-time, just prior to its actual consumption. Specifically, VO e-service consumer confidence level is informed, by leveraging third party objective evidence. This evidence comprises a set of Corporate Governance (CG) scores. These scores are used as a trust proxy for the "real" owner of the VO. There are also inherent limitations associated with the use of CG scores. These are duly acknowledged

Security and Privacy Issues in Cloud Computing

Cloud computing transforming the way of information technology (IT) for consuming and managing, promising improving cost efficiencies, accelerate innovations, faster time-to-market and the ability to scale applications on demand (Leighton, 2009). According to Gartner, while the hype grew ex-ponentially during 2008 and continued since, it is clear that there is a major shift towards the cloud computing model and that the benefits may be substantial (Gartner Hype-Cycle, 2012). However, as the shape of the cloud computing is emerging and developing rapidly both conceptually and in reality, the legal/contractual, economic, service quality, interoperability, security and privacy issues still pose significant challenges. In this chapter, we describe various service and deployment models of cloud computing and identify major challenges. In particular, we discuss three critical challenges: regulatory, security and privacy issues in cloud computing. Some solutions to mitigate these challenges are also proposed along with a brief presentation on the future trends in cloud computing deployment

E-infrastructures fostering multi-centre collaborative research into the intensive care management of patients with brain injury

Clinical research is becoming ever more collaborative with multi-centre trials now a common practice. With this in mind, never has it been more important to have secure access to data and, in so doing, tackle the challenges of inter-organisational data access and usage. This is especially the case for research conducted within the brain injury domain due to the complicated multi-trauma nature of the disease with its associated complex collation of time-series data of varying resolution and quality. It is now widely accepted that advances in treatment within this group of patients will only be delivered if the technical infrastructures underpinning the collection and validation of multi-centre research data for clinical trials is improved. In recognition of this need, IT-based multi-centre e-Infrastructures such as the Brain Monitoring with Information Technology group (BrainIT - www.brainit.org) and Cooperative Study on Brain Injury Depolarisations (COSBID - www.cosbid.de) have been formed. A serious impediment to the effective implementation of these networks is access to the know-how and experience needed to install, deploy and manage security-oriented middleware systems that provide secure access to distributed hospital based datasets and especially the linkage of these data sets across sites. The recently funded EU framework VII ICT project Advanced Arterial Hypotension Adverse Event prediction through a Novel Bayesian Neural Network (AVERT-IT) is focused upon tackling these challenges. This chapter describes the problems inherent to data collection within the brain injury medical domain, the current IT-based solutions designed to address these problems and how they perform in practice. We outline how the authors have collaborated towards developing Grid solutions to address the major technical issues. Towards this end we describe a prototype solution which ultimately formed the basis for the AVERT-IT project. We describe the design of the underlying Grid infrastructure for AVERT-IT and how it will be used to produce novel approaches to data collection, data validation and clinical trial design is also presented