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 January 2020
Start date: April 2020
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).
This opportunity is now closed. View our other opportunities.
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.
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.
Lead supervisorSchool of Computer Science and Electronic Engineering, University of Essex
Borzoo Rassouli received his MSc degree from university of Tehran, Iran in 2012, and a PhD in communications engineering from Imperial College London in 2016. He was a postdoctoral research associate at Imperial College from 2016 to 2018. In August 2018 he joined the University of Essex as a lecturer (Assistant Professor). His research interests are information theory and statistics.
Co-supervisorSchool 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.
Co-supervisorSchool 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.
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.
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:
You can find the terms and conditions of this studentship here (PDF).