Improving User Privacy in Mobile and Ubiquitous Health Technologies

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Our world is increasingly moving online, and healthcare is no exception. The use of mobile and ubiquitous health(mHealth and uHealth) technologies - devices that can assist with monitoring and managing the health of anindividual such as smartphones, smart home assistants and wearable sensors - has increased massively in recentyears and millions of people now own devices capable of collecting data and making inferences about their healthand wellbeing. While this is of benefit to the user, for example by making it easier to manage a health condition or tomotivate them to reach fitness goals, this continuous data collection can also negatively impact an individual'sprivacy with real-world consequences - such as being denied health insurance based on inferences made from suchdata or a fitness tracking app revealing the location of an individual's home to their social network. Another potentialconsequence is the loss of behavioural privacy. A smart home assistant designed for individuals to check up onelderly relatives, for example, may also be revealing information about that relative's daily routines and activities.Previous research has shown that individuals struggle with privacy decision making and thus may make decisionsthat do not align with their privacy preferences. The aim of this project is to reduce this burden by enabling individualsto make informed choices. Theories such as communication privacy management and privacy calculus seek toexplain the decision making process and will be useful in the development of a model to assist users with providingconsent, making disclosure decisions, and determining how their data should be used. Both communication privacymanagement and privacy calculus use the idea of balancing costs and benefits of data disclosure to make a decision.This is something that is difficult for individuals to do, particularly when the costs and benefits are not made explicit,hence the need for automated assistance.The initial focus of this project will be gaining an understanding of users' privacy expectations and preferences formHealth and uHealth technologies, followed by studies into how users understand and perceive privacy propertiesand risks of such systems. This knowledge will then be used to develop a model for facilitating informed decisionmaking within these technologies, with further research aimed at evaluating and refining this model. The goal is tomake sure users are fully aware of factors such as what data is being shared, with whom, and what information maybe inferred from this data in order to help them make an informed decision.This project is funded by the EPSRC via the Centre for Doctoral Training in Cyber Security (Trust, Identity, Privacyand Security at Scale) and is most aligned with the 'privacy' aspect of the CDT. The CDT's 'at scale' focus is alsoreflected in the ubiquitous nature of mHealth and uHealth technologies, and the pervasive large-scale data collectionthey facilitate.