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The problem of anonymisation on graph data

  • Full or part time
  • Application Deadline
    Sunday, February 23, 2020
  • Competition Funded PhD Project (European/UK Students Only)
    Competition Funded PhD Project (European/UK Students Only)

Project Description

Applications are invited for a three year PhD to commence in October 2020.

The PhD will be based in the Faculty of Technology, and will be supervised by Dr Janka Chlebikova. This is a joint project with Dauphine University, Paris.

The work on this project will involve:
● theoretical research in the specified area, including formulation and verification of new approaches
● an opportunity to undertake a research visit (2-4 months) in Dauphine University, Paris


The problem of anonymisation of data has received significant attention over the past decade. To find good methods to protect data privacy becomes important task attracting lots of research interest.

Graph data can be used to represent networks, e.g. social networks like Facebook or LinkedIn, but also spreading of infectious diseases. The representation is based on graphs, where vertices correspond to the entities and edges reflect relationships between entities. Since graph data in the networks may be sensitive, sharing such type of data requires the use of various anonymisation techniques. To achieve that, the idea is to preserve significant structural properties of the networks while ensuring anonymity of its entities. It means, a simple modification of the network’s properties is allowed to create several indistinguishable entities. Several anonymisation techniques have already been explored: generalisation group’s records following certain criteria in order to hide individual records, data perturbation, or k-anonymisation where each entity is considered anonymous if it is indistinguishable from at least (k-1) others. Some of these models were also studied from an algorithmic point of view, as anonymisation by using clustering methods. Also the computation complexity of k-anonymisation have been studied including various heuristics to modify graphs using allowed vertex/edge operations.

The purpose of the PhD is to study anonymisation models represented on graph data transformed to the problems from graph theory, mainly from an algorithmic point of view.

This is a joint project with Dauphine University, Paris. During the PhD there is an opportunity to undertake a research visit (2-4 months) in the partner institution.


General admissions criteria
You’ll need an upper second class honours degree from an internationally recognised university or a Master’s degree in an appropriate subject. In exceptional cases, we may consider equivalent professional experience and/or qualifications. English language proficiency at a minimum of IELTS band 6.5 with no component score below 6.0.

Specific candidate requirements
Good background in theoretical computer science, with a specialisation in combinatorial optimisation
Good background in discrete mathematics, especially in graph theory.

How to Apply
We’d encourage you to contact Dr Janka Chlebikova () to discuss your interest before you apply, quoting the project code.

When you are ready to apply, you can use our online application form. Make sure you submit a personal statement, proof of your degrees and grades, details of two referees, proof of your English language proficiency and an up-to-date CV. An extended statement as to how you might address the proposal would be welcomed.

Our ‘How to Apply’ page offers further guidance on the PhD application process.

If you want to be considered for this funded PhD opportunity you must quote project code COMP4500220 when applying.

Funding Notes

Candidates applying for this project may be eligible to compete for one of a small number of bursaries available; these cover tuition fees at the UK/EU rate for three years and a stipend in line with the UKRI rate (£15,009 for 2019/2020). Bursary recipients may be eligible for £1,500 for project costs/consumables.

How good is research at University of Portsmouth in Mathematical Sciences?

FTE Category A staff submitted: 11.00

Research output data provided by the Research Excellence Framework (REF)

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