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，它调查如何使用的智能手机应用程序（应用程序）可能会重塑用户的隐私观念，这种重塑的含义是什么。最近有研究隐私泄露和潜在防御机制的工作。然而，到目前为止，对这种隐私损失的后果只有有限的了解，特别是当大量隐私信息从智能手机用户在许多应用程序中泄露时。该项目旨在调查移动的技术（即，智能手机和智能手机应用程序）可以通过考虑用户的社会关系来揭示用户的个人信息并确定侵犯隐私的后果。该项目有助于深入了解移动的设备时代的用户隐私，并进一步开发适当的保护机制。智能手机用户的隐私在不同层次上进行了分析，包括个人，社会和社区关系的基础上不同程度的信息泄漏。基于大规模的跟踪调查和实验研究，发展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。数据可视化工具的开发，以捕捉和显示的时空模式和汇总统计的不同类型的隐私泄漏在真实的时间，这有助于用户获得更好的洞察潜在的隐私损失。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，并确定鼓励关注或忽视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.