Date of Award
Doctor of Philosophy
Qing Cao, Scott Ruoti, Jinyuan Sun, Wenjun Zhou
Mobile location data are ubiquitous in the digital world. People intentionally and unintentionally generate numerous location data when connecting to cellular networks or sharing posts on social networks. As mobile devices normally choose to communicate with nearby cell towers outdoor, it is reasonable to infer human locations based on cell tower coordinates. Many social networking platforms, such as Twitter, allow users to geo-tag their posts optionally, publishing personal locations to friends or everyone. These location data are particularly useful for understanding mobile usage behaviors and human mobility patterns. Meanwhile, the public expresses great concern about the privacy and security of their location information. Secure sharing of locations and mitigating malicious location queries from bots (especially on mobile devices) become increasingly imperative and necessary.
For mobile location analytics, we first study the cellular traffic data generated during the communication between mobile apps and nearby cell towers. We propose a multi-level mixture of kernel density estimation (mlKDE) model to profile the geospatial distribution of given individual apps, and demonstrate that mlKDE could effectively and robustly characterize the distributions of app usage in the real world. Then we investigate the location information shared publicly by social media users. Several Twitter case studies are conducted to illustrate how social location data inferred human mobility as well as geospatial distribution patterns.
To enhance the security of private locations, we propose homomorphic bloom filters to enable one party to determine, in a private and secure manner, whether or not the trajectory of a second party has an intersection with specific locations of interest. As location-based services are mainly operated on mobile devices, location queries are frequently launched by mobile users. To distinguish humans and bots, we design and implement SenCAPTCHA, a mobile-first CAPTCHA using orientation sensors. SenCAPTCHA works by showing users an animal image and asking them to tilt their devices to guide a red ball into the center of that animal’s eye. Two usability studies show that SenCAPTCHA is an "enjoyable" CAPTCHA and it is preferred by the majority of participants to other existing CAPTCHA systems.
Feng, Yunhe, "Mobile Location Data Analytics, Privacy, and Security. " PhD diss., University of Tennessee, 2020.