Postgraduate Research Opportunity

Privacy-assuring signal processing for the release of healthcare data

Details

Title: SCH13: Privacy-assuring signal processing for the release of healthcare data

Funding: Full time Home/EU fees and a stipend of £15,009 p.a. (terms & conditions)

Application deadline: 31 May 2019

Start date: October 2019

Duration: 3 years (full time)

Location: Colchester Campus

Based in: School of Computer Science and Electronic Engineering (in collaboration with the School of Health and Social Care).

Overview

This project is concerned with issues around data privacy in relation to the sharing of healthcare patient information/signal.

From a mathematical point of view, unless there is no statistical correlation/dependence between the shared signal and the private information, we are faced with the so-called "leakage" of privacy, which can be further analyzed in the context of signal processing, information theory and statistics. Hence, any disclosure of personal health-related data to legitimate entities (such as healthcare providers) to receive some utility in return, e.g., in the form of better health monitoring services, comes at the expense of a possible loss of privacy, which may have unintended and potentially adverse effects to the patients/users.

As a basic example, we focus on a wireless sensor network composed of health monitors attached to a patient’s body to measure his/her heart signal (along with other vital signals) continuously over a period of time, and this signal is automatically sent to a company in order to receive an analysis of how his/her heart functions. From this raw data, the company could also extract some information about the patients sleeping schedule (e.g. whether they are a night worker), and/or exercise schedule (if any).

This information puts the user's privacy at risk, as it has commercial value (e.g. could potentially be used for the advertisement of sleep- and/or exercise-related products) that the data company could exploit. So, how to process the information carrying signals (aggregate, filter, etc), and how to perform the transmission are of crucial importance.

Funding

The award consists of a full Home/EU fee waiver or equivalent fee discount for overseas students (further fee details), a doctoral stipend equivalent to the Research Councils UK National Minimum Doctoral Stipend (£15,009 in 2019-20), plus £2,500 training bursary via Proficio funding, which may be used to cover the cost of advanced skills training including conference attendance and travel.

Supervisors

Dr Leila Musavian

Co-supervisor

School of Computer Science and Electronic Engineering, University of Essex

Leila Musavian (IEEE S’05-M’07) is currently working as a Reader in Telecommunications and deputy director of research at the School of Computer Science and Electrical Engineering, University of Essex. She is a highly cited researcher also serving as a member of editorial board in multiple journals in telecommunications. Dr Musavian’s research interests include 5G/B5G, URLLC and radio resource allocation.

Professor Gill Green

Co-supervisor

School of Health and Social Care, University of Essex

Gill Green is a Professor of Medical Sociology in the School of Health and Social Care, University of Essex. Gill has been researching aspects of chronic illness since early 1990s specifically the impact illness has on people’s lives. Gill works closely with the National Institute of Health Research, specifically the Research Design Service and INVOLVE.

Criteria

The ideal candidate will have a strong background in mathematics, wireless communications and programming. A background or interest in learning about information theory, machine learning, and blockchain is also desirable.

How to apply

You can apply for this postgraduate research opportunity online.

Please include your CV, covering letter, personal statement, and transcripts of UG and Masters degrees in your application.

The University has moved to requiring only one reference for PhD applications and these can be received after a conditional offer has been made so the absence of these will not hold up the recruitment process.

Instruction to applicants

When you apply online you will be prompted to fill out several boxes in the form:

  • For "Course title" please put "PhD Electronic Systems Engineering"
  • For "Proposed research topic or area of research" please put the title of this studentship (SCH13 Privacy-assuring signal processing for the release of healthcare data)
  • For "If you have contacted a potential supervisor..." please put the name of the lead supervisor (Dr Borzoo Rassouli)

If you have any informal queries about this opportunity please email the lead supervisors, Dr Borzoo Rassouli (b.rassouli@essex.ac.uk) and Dr Hamed Ahmadi (hamed.ahmadi@essex.ac.uk).

You can find the terms and conditions of this studentship here (PDF).