Cloud computing promises to revolutionise how companies, research institutions and government organisations, including the National Health Service (NHS), offer applications and services to users in the digital economy. By consolidating many services as part of a shared ICT infrastructure operated by cloud providers, cloud computing can reduce management costs, shorten the deployment cycle of new services and improve energy efficiency. For example, the UK government's G-Cloud initiative aims to create a cloud ecosystem that will enable government organisations to deploy new applications rapidly, and to share and reuse existing services. Citizens will benefit from increased access to services, while public-sector ICT costs will be reduced. Security considerations, however, are a major issue holding back the widespread adoption of cloud computing: many organisations are concerned about the confidentiality and integrity of their users' data when hosted in third-party public clouds. Today's cloud providers struggle to give strong security guarantees that user data belonging to cloud tenants will be protected "end-to-end", i.e. across the entire workflow of a complex cloud-hosted distributed application. This is a challenging problem because data protection policies associated with applications usually require the strict isolation of certain data while permitting the sharing of other data. As an example, consider a local council with two applications on the G-Cloud: one for calculating unemployment benefits and one for receiving parking ticket fines, with both applications relying on a shared electoral roll database. How can the local council guarantee that data related to unemployment benefits will never be exposed to the parking fine application, even though both applications share a database and the cloud platform? The focus of the CloudSafetNet project is to rethink fundamentally how platform-as-a-service (PaaS) clouds should handle security requirements of applications. The overall goal is to provide the CloudSafetyNet middleware, a novel PaaS platform that acts as a "safety net", protecting against security violations caused by implementation flaws in applications ("intra-tenant security") or vulnerabilities in the cloud platform itself ("inter-tenant security"). CloudSafetyNet follows a "data-centric" security model: the integrity and confidentiality of application data is protected according to data flow policies -- agreements between cloud tenants and the provider specifying the permitted and prohibited exchanges of data between application components. It will enforce data flow policies through multiple levels of security mechanisms following a "defence-in-depth" strategy: based on policies, it creates "data compartments" that contain one or more components and isolate user data. A small privileged kernel, which is part of the middleware and constitutes a trusted computing base (TCB), tracks the flow of data between compartments and prevents flows that would violate policies. Previously such information flow control (IFC) models have been used successfully to enhance programming language, operating system and web application security. To make such a secure PaaS platform a reality, we plan to overcome a set of research challenges. We will explore how cloud application developers can express data-centric security policies that can be translated automatically into a set of data flow constraints in a distributed system. An open problem is how these constraints can be tied in with trusted enforcement mechanisms that exist in today's PaaS clouds. Addressing this will involve research into new lightweight isolation and sand-boxing techniques that allow the controlled execution of software components. In addition, we will advance software engineering methodology for secure cloud applications by developing new software architectures and design patterns that are compatible with compartmentalised data flow enforcement.

The financial services industry is at the forefront of the digital economy, and is crucial to the UK's, and especially London's, continuing social and economic prosperity. State-of-the-art Financial IT, Computational Finance and Financial Engineering (collectively Financial Computing) research is crucial to our international competitiveness in investment banking, investment funds or retail banking. Academically this DTC focuses on financial computing, as distinct from quantitative finance, already well resourced. Banks and funds view PhD students in science and engineering as an increasingly important and largely untapped talent pool; although one regrettably with little knowledge of finance. The Financial Services Skills Council notes that employers are placing increasing importance on high-level analytical skills, as well as their acute shortage, especially in the newly emerging areas that drive sector growth. This centre completely embraces the spirit of the Digital Economy programme. The proposed DTC is inherently multidisciplinary involving UCL Computer Science, one of the largest leading departments in its field in the UK, with LSE Finance and the London Business School; the two leading academic finance centres in the UK. Key to developing the financial services industry in the Digital Economy is the creation of a new cohort of researchers who have a strong research capability in IT and computation, but also understand finance and the needs of the wholesale financial services industry leading to early adoption of new financial information technology research.The research groups and centres that will participate in this DTC include worldclass groups at: UCL, such as the Software Systems Engineering Group and the Centre for Computational Statistics and Machine Learning, at LSE such as Financial Markets Group, and at the London Business School, including the Management Science and Operations and Finance Subject Areas. The total value of active grants currently held by the participating groups and centres exceeds 20 Million Pounds, and the number of currently registered PhD students exceeds 130. Collaborators in Statistics, Economics, Mathematics and Physics supplement the potential Supervisor pool.A great strength of this DTC proposal is our industry partners, which include: Abbey, Barclays, Barclays Capital, BNP Paribas, Credit Suisse, Deutsche Bank, Goldman Sachs, HSBC, Lloyds TSB, Man Investments, Merrill Lynch, Morgan Stanley, Nomura, RBS and Thomson Reuters. Regarding training and supervision, each DTC PhD student will follow a personally tailored programme of postgraduate courses drawn from the partners covering financial IT, networks & communications, HCI, computational finance, financial engineering and business, supplemented by lectures from our industry partners: * A tailored educational programme comprising graduate-level courses from UCL, LSE and LBS. * An academic supervisor (from UCL, LSE or LBS) and an industrial advisor (a partner bank, fund or Reuters), and a programme of research covered by an MOU. * A research project in financial IT, computational finance or financial engineering. * Training in industry software, such as Reuters 3000 Xtra, through UCL's virtual training floor.* A substantial period of industrial placement as agreed between the academic and industrial supervisors.* A short period at a leading foreign academic centre

Lancaster University (LU) proposes a Centre for Doctoral Training (CDT) to develop international research leaders in statistics and operational research (STOR) through a programme in which cutting-edge industrial challenge is the catalyst for methodological advance. Our proposal addresses the priority area 'Statistics for the 21st Century' through research training in cutting-edge modelling and inference for large, complex and novel data structures. It crucially recognises that many contemporary challenges in statistics, including those arising from industry, also engage with constraint, optimisation and decision. The proposal brings together LU's academic strength in STOR (>50FTE) with a distinguished array of highly committed industrial and international academic partners. Our shared vision is a CDT that produces graduates capable of the highest quality research with impact and equipped with an array of leadership and other skills needed for rapid career progression in academia or industry. The proposal builds on the strengths of an existing EPSRC-funded CDT that has helped change the culture in doctoral training in STOR through an unprecedented level of engagement with industry. The proposal takes the scale and scientific ambition of the Centre to a new level by: * Recruiting and training 70 students, across 5 cohorts, within a programme drawing on industrial challenge as the catalyst for research of the highest quality; * Ensuring all students undertake research in partnership with industry: 80% will work on doctoral projects jointly supervised and co-funded by industry; all others will undertake industrial research internships; * Promoting a culture of reproducible research under the mentorship and guidance of a dedicated Research Software Engineer (industry funded); * Developing cross-cohort research-clusters to support collaboration on ambitious challenges related to major research programmes; * Enabling students to participate in flagship research activities at LU and our international academic partners. The substantial growth in data-driven business and industrial decision-making in recent years has signalled a step change in the demand for doctoral-level STOR expertise and has opened the skills gap further. The current CDT has shown that a cohort-based, industrially engaged programme attracts a diverse range of the very ablest mathematically trained students. Without STOR-i, many of these students would not have considered doctoral study in STOR. We believe that the new CDT will continue to play a pivotal role in meeting the skills gap. Our training programme is designed to do more than solve a numbers problem. There is an issue of quality as much as there is one of quantity. Our goal is to develop research leaders who can innovate responsibly and secure impact for their work across academic, scientific and industrial boundaries; who can work alongside others with different skills-sets and communicate effectively. An integral component of this is our championing of ED&I. Our external partners are strongly motivated to join us in achieving these outcomes through STOR-i's cohort-based programme. We have little doubt that our graduates will be in great demand across a wide range of sectors, both industrial and academic. Industry will play a key role in the CDT. Our partners are helping to co-design the programme and will (i) co-fund and co-supervise doctoral projects, (ii) lead a programme of industrial problem-solving days and (iii) play a major role in leadership development and a range of bespoke training. The CDT benefits from the substantial support of 10 new partners (including Morgan Stanley, ONS Data Science Campus, Rolls Royce, Royal Mail, Tesco) and continued support from 5 existing partners (including ATASS, BT, NAG, Shell), with many others expected to contribute.