Date of Award
Spring 2025
Document Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
Department
Computer Science
First Advisor
Debbie Perouli,
Second Advisor
Michael Zimmer
Third Advisor
Praveen Madiraju
Fourth Advisor
Thomas Kaczmarek
Abstract
The digital advertising capabilities to reach users with personalized ads have been steadily improving over the last couple of decades. Automated mechanisms use efficient algorithms and a wealth of data to complete transactions between websites/apps and potential advertisers as part of what is known as programmatic advertising. Among the most prevalent protocols is Real Time Bidding (RTB), which selects ads for a user visiting a website in real-time through a series of messages within online ad exchanges. Such communications have the potential to carry detailed, personal information about users without their knowledge and have raised privacy concerns. RTB also poses a challenge for legislative bodies in countries abiding by modern privacy regulations as a complicated ecosystem with multiple players behind closed doors. This dissertation discusses a field study done to understand the user perception of usability, security, and privacy towards new technologies. That would create a foundation for further studies on how users with different demographics embrace new tech products versus how protective they are of privacy incorporated with new techs. Secondly, it discusses RTB privacy issues and surveys-related articles to show the need for the research community to develop innovative measurement techniques and shed light on an important but little-explored problem. Our experiments are designed to investigate the existing privacy issues in RTB to find evidence-based claims as well as to find possible other privacy issues. We proved that the RTB ecosystem is a closed system that gives very limited access to outsiders, which makes really hard for researchers to continue working. We would say that the OpenRTB protocol can be used to manipulate rules as it has loopholes. We also augment existing literature by observing ads and cookies related to the online behavior of artificial personas we created. Our results show that inappropriate ads can sometimes be shown to an audience.