PhD opportunity: Mathematical Methods for Differential Privacy in Clinical Research, University of Bath
When working with patient data in a healthcare setting, respecting patient consent and privacy is imperative and subject to strong legal and regulatory constraints. Moreover, not all data collected in modern clinical studies are uniformly suited to perform anonymization / de-identification – for example, only a small number of genetic markers, or peripheral information present in medical images such as MRIs may be sufficient to uniquely re-identify individuals contributing information to a dataset.
Differential privacy defines a framework to protect patient privacy when computing data summaries for datasets containing sensitive personal information. This PhD project, co-funded by Novartis, will focus on developing methods which can adapt their privacy budget to settings occurring in clinical trial / life-science scenarios, to gain meaningful insights into diseases whilst ensuring patient data privacy. The key idea will be to explore mechanisms through which we can improve the analytic utility of our datasets with a limited budget for privacy. We will probabilistic / statistical approaches, as well as randomization and machine learning to achieve these aims.
Further details can be found here:
https://www.findaphd.com/phds/project/mathematical-methods-for-differential-privacy-in-clinical-research/?p128141
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