Learning preferences for personalisation in a pervasive environment

With ever increasing accessibility to technological devices, services and applications there is also an increasing burden on the end user to manage and configure such resources. This burden will continue to increase as the vision of pervasive environments, with ubiquitous access to a plethora of resources, continues to become a reality. It is key that appropriate mechanisms to relieve the user of such burdens are developed and provided. These mechanisms include personalisation systems that can adapt resources on behalf of the user in an appropriate way based on the user's current context and goals. The key knowledge base of many personalisation systems is the set of user preferences that indicate what adaptations should be performed under which contextual situations. This thesis investigates the challenges of developing a system that can learn such preferences by monitoring user behaviour within a pervasive environment. Based on the findings of related works and experience from EU project research, several key design requirements for such a system are identified. These requirements are used to drive the design of a system that can learn accurate and up to date preferences for personalisation in a pervasive environment. A standalone prototype of the preference learning system has been developed. In addition the preference learning system has been integrated into a pervasive platform developed through an EU research project. The preference learning system is fully evaluated in terms of its machine learning performance and also its utility in a pervasive environment with real end users

Personalised privacy in pervasive and ubiquitous systems

Our world is edging closer to the realisation of pervasive systems and their integration in our everyday life. While pervasive systems are capable of offering many benefits for everyone, the amount and quality of personal information that becomes available raise concerns about maintaining user privacy and create a real need to reform existing privacy practices and provide appropriate safeguards for the user of pervasive environments. This thesis presents the PERSOnalised Negotiation, Identity Selection and Management (PersoNISM) system; a comprehensive approach to privacy protection in pervasive environments using context aware dynamic personalisation and behaviour learning. The aim of the PersoNISM system is twofold: to provide the user with a comprehensive set of privacy protecting tools and to help them make the best use of these tools according to their privacy needs. The PersoNISM system allows users to: a) configure the terms and conditions of data disclosure through the process of privacy policy negotiation, which addresses the current “take it or leave it” approach; b) use multiple identities to interact with pervasive services to avoid the accumulation of vast amounts of personal information in a single user profile; and c) selectively disclose information based on the type of information, who requests it, under what context, for what purpose and how the information will be treated. The PersoNISM system learns user privacy preferences by monitoring the behaviour of the user and uses them to personalise and/or automate the decision making processes in order to unburden the user from manually controlling these complex mechanisms. The PersoNISM system has been designed, implemented, demonstrated and evaluated during three EU funded projects