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Privacy-assuring signal processing for the release of healthcare data

Project Description

Application deadline: Monday 28 October 2019
Start date: January 2020
Lead department: School of Computer Science and Electronic Engineering
Supporting department: School of Health and Social Care

Studentship 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 analysed 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, eg 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 (eg 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 (eg 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.

Primary supervisor

• Borzoo Rassouli received an MSc degree from university of Tehran, Iran, in 2012, and a PhD in communications engineering from Imperial College London, UK, 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.

Supporting supervisors

• Hamed Ahmadi (SM IEEE) is a lecturer in Wireless communications and networks in the School of Computer Science and Electrical Engineering at the 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 at the University of Essex.
• Gill Green is a Professor of Medical Sociology in the School of Health and Social Care at the University of Essex.

Funding Notes

This award consists of a full Home/EU fee waiver or equivalent fee discount for overseas students (see for 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.

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