EAGER: Collaborative Research: Towards Understanding Smartphone User Privacy: Implication, Derivation, and Protection

该项目旨在解决智能手机用户的隐私问题。特别是，它调查了智能手机应用程序的使用如何重塑用户的隐私观念，以及这种重塑的含义。最近有研究调查了隐私泄露和潜在的防御机制。然而，到目前为止，人们对这种隐私损失的后果知之甚少，特别是当智能手机用户的大量隐私信息在许多应用程序中泄露时。该项目旨在调查移动技术（即智能手机和智能手机应用程序）如何通过考虑用户的社会关系来揭示用户的个人信息并识别侵犯隐私的后果。该项目有助于深入了解移动设备时代的用户隐私，并进一步制定适当的保护机制。基于不同程度的信息泄露，分析了智能手机用户在不同层面的隐私，包括个人、社会和社区关系。基于大规模的痕迹驱动调查和实验研究，建立了贝叶斯网络和隐马尔可夫模型等统计模型来理解用户的时间隐私泄露模式。开发数据可视化工具，实时捕捉和显示不同类型隐私泄露的时空格局和汇总统计数据，帮助用户更好地了解潜在的隐私损失。统计建模和数据可视化技术进一步使社会科学家能够研究隐私侵犯的心理或社会后果，并确定鼓励关注或不关注智能手机用户隐私的因素。

This project aims to address privacy concerns of smartphone users. In particular, it investigates how the usages of the smartphone applications (apps) may reshape users' privacy perceptions and what is the implication of such reshaping. There has been recent work that investigates privacy leakage and potential defense mechanisms. However, so far there is only limited understanding on the consequences of such privacy losses, especially when large amount of privacy information leaked from smartphone users across many apps. The project seeks to investigate how the mobile technology (i.e., smartphone and smartphone apps) can reveal users' personal information and identify the consequences of privacy violations, by taking users' social relationships into consideration. The project facilitates a deep understanding of user privacy in the age of mobile devices and further develops appropriate protective mechanisms. Smartphone user privacy across different levels are analyzed including individual, social and community relationships based on different levels of information leakage. Statistical models, such as Bayesian networks and hidden Markov models, are developed to understand users' temporal privacy leakage patterns based on large-scale trace-driven investigation and experimental study. Data visualization tools are developed to capture and display the spatial-temporal patterns and summary statistics of different types of privacy leakage in real time, which helps users gain better insights on potential privacy losses. The statistical modeling and the data visualization techniques further enable the social scientists to study the psychological or social consequences of privacy violations, and identify factors encouraging attention or inattention to smartphone user privacy.