Date of Award
5-2025
Document Type
Thesis
Degree Name
Master of Science (MS)
Department
Computer Science
Committee Chair/Advisor
Mert D. Pesé
Committee Member
Long Cheng
Committee Member
Lu Yu
Abstract
Modern vehicles have become sophisticated computational and sensor systems, as evidenced by advanced driver assistance systems (ADAS), in-car infotainment, and autonomous driving capabilities. They collect and process vast amounts of data through various onboard subsystems. One significant player in this landscape is Android Automotive OS (AAOS), which has been integrated into over 100 million vehicles and has become a dominant force in the in-vehicle infotainment (IVI) market. With this extensive data collection, privacy concerns have become increasingly crucial. The volume of data gathered by these systems raises questions about how this information is stored, used, and protected, making privacy a critical issue for manufacturers and consumers. However, very little has been done on vehicle data privacy. This paper focuses on the privacy implications of AAOS by examining the exact nature and scope of its data collection. It also presents a novel automotive privacy analysis tool called PriDrive, which employs three methodological approaches: network traffic inspection, and both static and dynamic analyses of Android images using rooted emulators from various OEMs. Finally, the evaluation on three different OEM platforms reveals that some OEMs collect much more data than others. OEM A collects vehicle speed at a sampling rate of roughly 25 Hz. Meanwhile, other properties such as model info, climate and AC, seat data, and others are collected in a batch 30 seconds into vehicle startup.
Recommended Citation
Gözübüyük, Bulut, "Privacy Implications of Data Collection in Android Automotive OS" (2025). All Theses. 4533.
https://open.clemson.edu/all_theses/4533
Included in
Digital Communications and Networking Commons, Hardware Systems Commons, Other Computer Engineering Commons