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PhD Studentship: SCH13: Privacy-Assuring Signal Processing for the Release of Healthcare Data

University of Essex - School of Computer Science and Electronic Engineering and School of Health and Social Care

Qualification Type: PhD
Location: Colchester
Funding for: UK Students, EU Students, International Students
Funding amount: see below in advert for information
Hours: Full Time
Placed On: 7th June 2019
Closes: 28th June 2019

For PHD course and award start date of October 2019

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

Full Home/EU fee waiver or equivalent fee discount for overseas students (further fee details). Overseas students would be expected to provide the additional funding to cover the difference.

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.

Please address any additional questions and queries about this studentship to: Dr Hamed Ahmadi, (hamed.ahmadi@essex.ac.uk), Dr Borzoo Rassouli, (b.rassouli@essex.ac.uk), Professor Gill Green, (gillgr@essex.ac.uk), Dr Leila Musavian (leila.musavian@essex.ac.uk)

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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.

   
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