An increasing number of government services take advantage of new technological advances (eg. Cloud, IoT and Big Data), aiming to develop open and dynamic online services (eg. governmental documents issuing, such as passports), which improve citizen services and remove societal barriers to the adoption of such services. This had led to new challenges for computer scientists associated with information and data privacy management, technological complexity and restrictive laws and regulations.
From a technical perspective, the lack of control over management and privacy of citizens’ personal data and the lack of accountability are major issues. From a societal perspective, the lack of trust that citizens have for such services, and their perception on how governmental services store and deal with their data, together with the lack of transparency provide major barriers for the wide scale adoption of such services.
The societal perspective is as important as the technical since, unlike other types of online services, where users might decide to not use them or not to provide their data (eg. when they do not agree with their privacy policies and usage of data), in e-government services citizens usually do not have the option of refusing to provide their data, and they may even be obliged to use them by law. However, existing computational frameworks and platforms do not take into account citizens’ privacy needs and they fail to combine privacy with trust analysis in order to better understand how trust influences citizens’ needs and how it impacts potential privacy threat mitigation strategies. A higher level of trust is highly likely to increase the adoption of e-government services by the society.
This project will introduce a new Visual Privacy Management paradigm, which supports citizen trust through the creation, visualization and management of Privacy Level Agreements (PLAs). PLAs are a new concept suggested as part of the EU project VisiOn (in which the University of Brighton is a partner) and they aim to empower users to set the desired level of privacy, based on a simple to understand visualisation of the privacy level, giving them control over how their data will be used by online services. In particular, the project will develop a novel computational framework to enable users to set their desired level of privacy requirements and assist the visualisation of such requirements, therefore giving users the ability to control the usage of their data over a number of services. In particular, the project will focus on providing novel contributions in privacy management by developing new modelling 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 project will also investigate appropriate modelling and visualisation procedures to represent combined information privacy threats and to measure their effectiveness and applicability.