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