Studentship 1: Privacy Engineering
Information and communication technologies (ICT) play a significant role in everyday life. New technological advances such as cloud computing, the internet of things and big data provide benefits and have changed the way we store, access and exchange information. In fact, the rapid development and advances in ICT have led to their adaption by organisations (enabling them to transform their business to digital services, increasing their efficiency), public authorities (enabling them to provide new services to citizens and reduce complexity) and individuals (enabling them to communicate and share personal information faster and more efficiently). However, together with all the benefits that such technologies bring, opportunities (deliberate or accidental) for misuse of personal or confidential data are also created mostly due to lack of control over management and privacy issues.
This project will create a novel framework that will advance the state of the art in privacy by design through the creation, management and visualisation of privacy requirements and privacy enhancing technologies, which strengthen the transparency and trustworthiness of online services in modern society. In particular the project will focus on:
the development of modelling and reasoning techniques to elicit, understand and analyse privacy and trust-based risk management requirements, with particular emphasis on requirements associated with the dynamic nature of information privacy concerns
the integration of appropriate modelling and visualisation procedures to predict and represent combined information privacy threats and to measure their effectiveness and applicability
the impact of new technologies such as blockchain to privacy analysis and management.
The successful candidate will work alongside researchers working on national and international projects in the area of privacy engineering and will collaborate with large international organisations that specialise in security and privacy such as ATOS and Business-e. It is expected that some of the PhD project results will feed directly to those real-life projects.
Studentship 2: Interactive Visual Data Exploration
Visual analytics helps to develop knowledge from large scale data sets via human interaction with automatic and visual analysis methods. Whilst there are commercial data analytic products (eg tableau, qlik) empowering users to make use of permitted visualisations, the development of open-source software with modifiable functionality can be of significant benefit in the development of new innovative methodological approaches to data analysis.
The project aims to develop a novel framework that uses combinations of interactive visualisations to optimally exploit data source features, whilst adhering to additional stakeholder desires, requirements and tasks. The framework will be used to guide advances within the development of visual exploration systems, which provide aesthetic appeal and tangible benefits arising from the use of spatial features of representations, or explanation facilities of underlying machine learning or analytical techniques. It is envisaged that the framework and exploration systems developed will be usable within different contexts, whilst specific tailoring may enhance their utility within specific domains.
This project will build upon past project experience of visual data exploration in an industrial context, in conjunction with researchers at the University of Salerno, Italy (see Visual Exploration System in an Industrial Context for more information). The project will additionally utilise domain expertise within the Advanced Engineering Centre (AEC) at Brighton, which is a key member of the UK’s low carbon Advanced Propulsion Centre.
The AEC has a strong track record of collaborative research and development at the forefront of the automotive domain, with global engineering companies like Ricardo PLC and BP. Automotive simulation and experimental research is now multi-disciplinary and generates increasingly large and complex data sets that require analysis and interpretation. In this project, the data sets, tasks and stakeholders in various engineering domains will be investigated in conjunction with domain experts based within the AEC.
Alongside the development of research skills within the project, the successful candidate will likely enhance their knowledge and understanding of important skills in the industrial sector, such as visualisations for analysis, machine learning, and mainstream big data technologies (eg Apache Hadoop or Apache Spark).