Vision: In this fellowship, I aim to address a major challenge in the adoption of user-centred privacy-enhancing technologies: Can we leverage novel architectures to provide private, trusted, personalised, and dynamically- configurable models on consumer devices to cater for heterogenous environments and user requirements? Importantly, such properties must provide assurances for the data integrity and model authenticity/trustworthiness, while respecting the privacy of the individuals taking part in training and improving such models. Innovation and adoption in this space require collaborations between device manufacturers, platform providers, network operators, regulators, and the users. The objectives of this fellowship will take us far beyond the status-quo, one-size-fits-all solutions, providing a framework for personalised, trustworthy, and confidential edge computing, with ability to respect dynamic policies, in particular when dealing with sensitive models and data from the consumer Internet of Things (IoT) devices. In this fellowship, I aim to address these challenges by designing and evaluating an ecosystem where analytics from, and interaction with, consumer IoT devices can happen with trust in the model and authenticity, while enabling auditing and personalisation, hence pushing today's boundaries on all-or-nothing privacy and enabling new economic models. This approach requires designing for capabilities beyond the current trusted memory and processing limitations of the devices, and a cooperative dialogue and ecosystem involving service providers, ISPs, regulators, device manufacturers, and the end users. By designing our framework around the latest architectural and security features in edge devices, before they become commercially available, we provision for Model Privacy and a User-Centred IoT ecosystem, where service providers can have trust in the authenticity, attestability, and trustworthiness of the valuable models running on user devices, without the users having to reveal sensitive personal information to these cloud-based centralised systems. This approach will enable advanced and sensitive edge-based analytics to be performed, without jeopardising the individuals' privacy. Importantly, we aim to integrate mechanisms for data authenticity and attestation into our proposed framework, to enable trust in models and the data used by them. Such privacy-preserving technologies have the capacity to enable new form of sensitive analytics, without sharing raw data and thereby providing legal balancing capabilities that might enable certain sensitive (or currently unlawful) data analysis.

Personalisation of media experiences for the individual is vital for audience engagement of young and old, allowing more meaningful encounters tailored to their interest, making them part of the story, and increasing accessibility. The goal of the BBC Prosperity Partnership is to realise a transformation to future personalised content creation and delivery at scale for the public at home or on the move. Evolution of mass-media audio-visual 'broadcast' content (news, sports, music, drama) has moved increasingly towards Internet delivery, which creates exciting potential for hyper-personalised media experiences delivered at scale to mass audiences. This radical new user-centred approach to media creation and delivery has the potential to disrupt the media landscape by directly engaging individuals at the centre of their experience, rather than predefining the content as with existing media formats (radio, TV, film). This will allow a new form of user-centred media experience which dynamically adapts to the individual, their location, the media content and producer storytelling intent, together with the platform/device and the network/compute resources available for rendering the content.The BBC Prosperity Partnership will position the BBC at the forefront of this 'Personalised Media' revolution enabling the creation and delivery of new services, and positioning the UK creative industry to lead future personalised media creation and intelligent network distribution to render personalised experiences for everyone anywhere. Realisation of personalised experiences at scale presents three fundamental research challenges: capture of object-based representations of the content to enable dynamic adaption for personalisation at the point of rendering; production to create personalised experiences which enhance the perceived quality of experience for each user; and delivery at scale with intelligent utilisation of the available network, edge and device resources for mass audiences. The BBC Prosperity Partnership will address the major technical and creative challenges to delivering user-centred personalised audience experiences at scale. Advances in audio-visual AI for machine understanding of captured content will enable the automatic transformation of captured 2D video streams to an object-based media (OBM) representation. OBM will allow adaptation for efficient production, delivery and personalisation of the media experience whilst maintaining the perceived quality of the captured video content. To deliver personalised experiences to audiences of millions requires transformation of media processing and distribution architectures into a hybrid and distributed low-latency computation platform, allowing flexible deployment of compute-intensive tasks across the network. This will achieve efficiency in terms of cost and energy use, while providing optimal quality of experience for the audience within the technical constraints of the system.