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PhD Studentship in Industrial Artificial Intelligence, “Efficient Privacy Preserving Scheme for Data Processing”, in collaboration with EIT-Digital and Delta Capita Ltd

In collaboration with the European Institute of Innovation and Technology (EIT) and Delta Capita Ltd, the School of Mathematics, Computer Science & Engineering is offering a PhD studentship.

The studentship falls under the new EIT-Digital Industrial Artificial Intelligence Doctoral Programme at City, University of London. The studentship consists of a full fee waiver and a stipend of £18K per year, for four years. As part of their studies and training, the student will spend time at City, University of London, EIT-Digital London Co-Location Centre and Delta Capita Ltd premises. In addition, over the 4 years of study, the PhD student will follow the EIT Digital PhD, which is an enrichment programme to develop skills and competences in innovation and entrepreneurship in digital technologies. The label programme supports: 1 between 3 and 6 months abroad to enrich their research experience, for which a supplementary budget is available; 2 EIT-Digital European training in innovation, entrepreneurship and digital transformation leadership, which takes place  at other EIT-Digital centres across Europe; 3 Business Development Experience, which is a series of activities planned together with the industrial supervisor to put in practice the knowledge acquired in the seminars.

The PhD project, entitled “Efficient Privacy Preserving Scheme for Data Processing”, is focused on the design, implementation and evaluation of an efficient scheme which will preserve user privacy, reduce the complexity of processing noise and allow multi-user access for machine learning and other applications. Current literature focuses on fully homomorphic encryption schemes which allow single user access under a unique key and substantially high noise of multiplication depth. Processing results of encrypted data has shown huge leaps but still lack practicality as the data sets grow. The scalability of the current encryption schemes remains unpractical; current results range from 5 minutes for 4 l-bit elements to just under 2 hours for 16 l-bit elements. Even a small reduction in the overall multiplicative complexity can have a major impact on how the circuits scale. Reducing the multiplicative depth of homomorphically encryption algorithms specifically for deep learning architectures is essential.

The appointed candidate will have a degree and a MSc in Mathematics or Computer Science or an area related to Secure Artificial Intelligence. Given the nature of the project, experience in industry is advisable.

Applications, consisting of a CV and a Personal Statement, should be submitted to Postgraduate Research Course Officer ( Closing date: 17:00 BST, 1 December 2019.

Interviews are scheduled for the week commencing the 9 December 2020.

The role is available from 1 January 2020 or earlier by negotiation.

Salary: 18,000 per year, 4 years plus travel budget

For further information about the post please contact