EAGER: Collaborative: PRICE: Using process tracing to improve household IoT users' privacy decisions

家用物联网(IoT)设备旨在收集家庭中的信息并相互通信，以创建支持我们日常活动的强大新应用程序。现有的研究表明，用户很难在这类设备上选择他们的隐私设置。该项目的目标是调查家庭物联网用户的隐私决策如何、为什么以及何时不是最优的，并利用此研究的见解创建和测试一个简单的单一用户界面，该界面集成了家庭内所有设备的隐私设置。这个界面将有一套灵活的隐私配置文件，符合广泛的隐私偏好，并帮助美国公司在欧洲市场运营时遵守欧盟的隐私法规。与现有的隐私研究不同，该项目旨在了解消费者的实际决策过程。现有的隐私研究假设用户采用有限但本质上合理的决策做法。这样，我们就可以利用部分价值效用映射和过程跟踪(包括眼球跟踪)来改进隐私决策的理论。这些努力的结果被用来开发一套由数据驱动的隐私配置文件，涵盖大多数用户的隐私偏好。然后将这些配置文件集成到隐私管理界面(对现有物联网管理平台的开源贡献)，该界面不会为用户提供过多的信息和控制，而是利用他们现有的决策流程并避免他们的偏见。

Household Internet-of-Things (IoT) devices are intended to collect information in the home and to communicate with each other, to create powerful new applications that support our day-to-day activities. Existing research suggests that users have a difficult time selecting their privacy settings on such devices. The goal of this project is to investigate how, why and when privacy decisions of household IoT users are suboptimal, and to use the insights from this research to create and test a simple single user interface that integrates privacy settings across all devices within a household. This interface will have a flexible set of privacy profiles that fits a wide range of privacy preferences and also helps US companies to comply with EU privacy regulations when they operate in the European market.Unlike existing privacy research, which assumes that users employ limited but essentially rational decision-making practices, this project aims to understand consumers' actual decision processes. In this way we can improve theories about privacy decision making using part-worth utility mapping and process tracing (including eye-tracking). The results of these efforts are used to develop a data-driven set of privacy profiles that cover the privacy preferences of most users. These profiles are then integrated into a privacy management interface (an open-source contribution to an existing IoT management platform) that does not overload users with information and control, but instead leverages their existing decision processes and avoids their biases.