“Consequence” was a collaborative research project partly funded by the European Commission, which ran between 2008 and 2011. The project built a comprehensive framework for controlled dissemination of information based on Data Sharing Agreements and a combination of technologies based on rights management and usage control policies. The framework was demonstrated within the context of sharing large scale scientific data and also for controlled sharing of information between first responder organisations in crisis management scenarios. More details, videos, papers and project deliverable are available from the Consequence web site.
The project aims to facilitate data sharing in cloud environments by providing end-to-end data centric security from the client to the cloud based on the (semi-)automated enforcement ofData Sharing Agreements. These agreements may reflect legal, contractual or user defined preferences, which may be conflicting and thus an appropriate balance and model for their enforcement must be found.
The project aims to study privacy management by investigating how individuals learn and benefit from their membership of social or functional groups, and how such learning can be automated and incorporated in modern mobile and ubiquitous technologies that increasingly pervade society. The project will focus on addressing the privacy concerns of individuals in the context of their use of pervasive technologies, such as Smartphones and Clouds and aims to contribute in three research areas: (1) engineering of adaptive systems that guide their users to manage their privacy; (2) the development of logic-based machine learning techniques to alleviate the cognitive and physical load of personalising users’ privacy requirements; and (3) empirical investigation of the privacy behaviour of and in groups, in the context of both collaboration and conflict. This is a joint project with The Open University and the University of Exeter, funded by the EPSRC (Grant No EP/K033425/1). At Imperial the project is led by Dr Alessandra Russo.
How will Big Data affect innovation, growth and well-being in the UK economy? By Big Data we mean very large or complex datasets that are constantly accumulating in society because of the dramatically increased ability to sense, capture, store and analyse information about social, economic, or scientific phenomena. We anticipate that Big Data and associated analytics may ultimately transform how societies and communities view themselves, and how governments, large corporations, and entrepreneurial startup companies relate to those populations. Therefore it is essential to consider how Big Data might contribute to economic growth and generate opportunities for innovation for UK companies. …
The Intel ICRI in Sustainable Connected Cities is a joint project between Intel, Imperial College London, and University College London. Its activities are concerned with how to enhance the social, economic and environmental well being of cities by advancing compute, communication and social constructs to deliver innovations in system architecture, algorithms and societal participation.
Our work within the initiative concerns mainly the security and resilience of Wireless Sensor Networks.
The project investigated privacy requirements, developing a variety of novel techniques to capture and elicit user activity as part of field studies that involved real mobile applications. These techniques were used in conjunction with traditional methods such as focus group studies, interviewing and online questionnaires. We tracked user behavior to see how people interacted particularly with social networks, but also with monitoring location information relating to friends and family. These requirements were used to produce a privacy management framework that enables users to specify privacy preferences, to help visualize them, to learn from the user’s behaviour what their likely preferences are, and to enforce privacy policies. From the perspective of the Imperial research team, the emphasis of the work was on learning privacy policies which can automate the privacy related actions taken by users, by monitoring their past behavior. This was complemented by the work of the OU research team, who focussed on developing techniques for eliciting and analysing privacy requirements for mobile applications; conducting field studies to gain an in-depth understanding of users’ privacy concerns and to evaluate technologies for enhancing end-user privacy management.
2013, Funded by EIT ICT Labs
Partners: Inria (Lead), Alcatel Lucent, Cap Digital, Imperial College, SAP, KTH, LiquidMedia
Crowd-sourcing, if used properly, is a very powerful instrument to collect, enrich and exploit data and knowledge in Digital Cities. The aim of this project is to build, deploy and assess novel services on crowd-sourcing platforms that integrate both data capture facilities and core services enabling the turstworthiness of the crowd to be dynamically evaluated, offering rich workflow modeling for hybrid processing and ensuring privacy guarantees on crowdsourced data.
Policy-based management has been proposed in recent years as a suitable means for managing Quality of Service (QoS) in IP networks. Yet despite research projects, standardisation efforts, and substantial interest from industry, policy-based management is still not a reality. One of the reasons for the reticence to adopt this technology is that it is difficult to analyse policies to determine that they will actually work, given the capabilities of managed network devices, and to guarantee the stability of the network configuration, given that policies may have conflicts leading to unpredictable effects. This project aims to address the challenges of policy analysis, policy validation and policy refinement within the specific application domain of Quality of Service for IP networks. …
Future e-science and e health applications will involve mobile users, possibly with on-body sensors interacting with a ubiquitous computing environment which detects their activity, current context and adapts accordingly. However, the promise of such ubiquitous computing environments will not be realised unless these systems can effectively disappear and for this they need to become autonomous by managingtheir own evolution and configuration changes without explicit user or administrator action. This project will develop the architecture, tools and techniques which permit these environments to become self-managing. To provide self-managment at varying levels (for individual devices, for simple body-areaor home-area networks, as well as large scale network infrastructures) we advocate the concept of a self managed cell (SMC) as the basic architecture pattern at both local and intergrated levels. We will define, prototype and evaluate architectures based on the SMC pattern and their use in e-health applications. To this end we will: define and implement the core SMC pattern in terms of the monitoring, service-discovery, context and policy-control services required for basic adaptation mechanisms, investigate how SMC’s can be dynamically structured into large structures and specialise SMC’s and their interactions for two e-health application scenarios.
This project was in Collaboration with Prof. Joe Sventek at the University of Glasgow