Improving User Privacy in Mobile and Ubiquitous Health Technologies

我们的世界越来越多地转向在线，医疗保健也不例外。近年来，移动的和无处不在的健康（mHealth和uHealth）技术（可以帮助监测和管理个人健康的设备，如智能手机，智能家居助理和可穿戴传感器）的使用大幅增加，数百万人现在拥有能够收集数据并推断其健康和福祉的设备。虽然这对用户有利，例如通过使其更容易管理健康状况或激励他们达到健身目标，但这种连续的数据收集也会对个人的隐私产生负面影响，从而产生现实世界的后果-例如基于从此类数据或健身跟踪应用程序中得出的推断而被拒绝健康保险，该应用程序将个人的家的位置透露给他们的社交网络。另一个潜在的后果是行为隐私的丧失。例如，一个为个人设计的智能家居助手，可以检查老年亲属，也可能会泄露有关该亲属的日常生活和活动的信息。以前的研究表明，个人在隐私决策方面很困难，因此可能会做出与他们的隐私偏好不一致的决定。该项目的目的是通过使个人能够做出知情的选择来减轻这一负担。通信隐私管理和隐私演算等理论试图解释决策过程，并将有助于开发一个模型，以帮助用户提供同意，做出披露决定，并确定如何使用他们的数据。通信隐私管理和隐私计算都使用平衡数据披露的成本和收益的想法来做出决定。这对个人来说很难做到，特别是当成本和收益不明确时，因此需要自动化的帮助。该项目的最初重点是了解用户对健康和uHealth技术的隐私期望和偏好，其次是研究用户如何理解和感知这些系统的隐私属性和风险。这些知识将用于开发一个模型，以促进在这些技术范围内做出明智的决策，进一步的研究旨在评估和完善这一模型。其目标是确保用户充分了解诸如哪些数据正在共享，与谁共享，以及从这些数据中推断出哪些信息等因素，以帮助他们做出明智的决定。该项目由EPSRC通过网络安全博士培训中心资助（信任，身份，隐私和大规模安全），并且与CDT的“隐私”方面最一致。CDT的“大规模”重点也反映在移动健康和uHealth技术的无处不在的性质，以及它们促进的普遍的大规模数据收集。

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.