Threats and opportunities of mobile sensing technology in personal privacy and public security

Abstract

OF THE DISSERTATION Threats and Opportunities of Mobile Sensing Technology in Personal Privacy and Public Security By CHEN WANG Dissertation Director: Yingying Chen The proliferation of the mobile devices (e.g., smartphones, smartwatches and fitness trackers) has brought great convenience to our daily lives. Mobile users can enjoy the online access anytime and anywhere through WiFi or cellular services, monitor daily activities (e.g., walking steps) via wearable devices, or flexibly access the devices via touch screens and microphones. The pervasive mobile sensors can further benefit the public sector, such as providing realtime data for public transportation, emergency and public safety protection. While the mobile technologies facilitate a wide range of useful applications to the users, an adversary may leverage them to derive the user’s sensitive private information. This dissertation focuses on exploring the security threats of the mobile devices given the various embedded sensors. Moreover, we explore to utilize the mobile sensing technologies as opportunities for protecting not only the personal privacy but also the public security. As the smartphone is the most popular mobile device worldwide, we first investigate to what extent the users’ personal information such as social relationships and demographics could be revealed from their smartphones, in particular through the simple signal information of the pervasive Wi-Fi Access Points (AP) without examining any Wi-Fi traffic. We successfully derive the users’ activities at daily visited places from the surrounding APs and utilize that as the basis to infer the users’ social interactions and individual behaviors. Our approaches capture how closely people interact with each other based on their physical closeness to infer their social relationships and recognize the individual behaviors via their activity characteristics (e.g., activeness and time slots) at their daily visited places to estimate the users’ demographics.