Improving User Privacy in Mobile and Ubiquitous Health Technologies

我们的世界越来越多地走向网络，医疗保健也不例外。近年来，移动和无处不在的健康(mHealth和uHealth)技术的使用大幅增加，数百万人现在拥有能够收集数据并对他们的健康状况做出推断的设备。这些技术可以帮助监测和管理个人的健康状况，如智能手机、智能家居助理和可穿戴传感器。虽然这对用户是有益的，例如，通过更容易地管理健康状况或激励他们实现健身目标，但这种持续的数据收集也会对个人的积极性产生负面影响，并产生现实世界的后果--例如，根据这些数据或健康跟踪应用程序向他们的社交网络显示个人家的位置，被拒绝提供医疗保险。另一个潜在的后果是行为隐私的丧失。例如，一款为个人检查年长亲戚而设计的智能家居助手也可能会泄露该亲戚的日常生活和活动信息。之前的研究表明，个人在隐私决策方面存在困难，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明智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，并将有助于开发一种模型，帮助用户提供同意、做出披露决策，以及确定他们的数据应该如何使用。通信隐私管理和隐私计算都使用平衡数据泄露的成本和收益的想法来做出决定。这是个人很难做到的事情，特别是在成本和收益不明确的情况下，因此需要自动辅助。该项目的初始重点将是了解用户对Health和UHealth技术的隐私期望和偏好，然后研究用户如何理解和感知此类系统的隐私属性和风险。然后，这些知识将被用来开发一个模型，以促进在这些技术中进行知情决策，并进行进一步研究，以评估和完善这一模型。该项目的目标是确保用户充分了解各种因素，比如共享什么数据，与谁共享，以及从这些数据中推断出什么信息，以帮助他们做出明智的决定。该项目由EPSRC通过网络安全(信任、身份、隐私和大规模安全)博士培训中心资助，与CDT的“隐私”方面最为一致。CDT的“规模”关注也反映在mHealth和uHealth技术的无处不在的性质，以及它们促进的无处不在的大规模数据收集。

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.