Friday, February 26th, 2021 | 12:00pm – 1:00pm | LINK
Ph.D. Candidate, Columbia University
Cybersecurity Lecture Series
Presented by the School of Cybersecurity and Privacy,
and the School of Electrical and Computer Engineering
Accurately analyzing and modeling online browsing behavior plays a key role in understanding users and technology interactions. Specifically, understanding whether users have correct perceptions of their browsing behavior will help to identify key features for models of user behavior, which will, in turn, enable realistic-looking synthetic data generation. In this work, we designed and conducted a user experiment to collect browsing behavior data from 32 participants continuously for 14 days. The collected dataset includes URLs of visited websites, actions taken on each website (such as clicking links or typing in a textbox), and timestamps of all activities. Finally, we use this new dataset to empirically address the following questions: (1) Do people have correct perceptions of their level of online behavior? (2) Do people alter their browsing behavior knowing that they are being tracked? (3) How do structural properties of browsing patterns vary across demographic groups?
Yuliia Lut is a Ph.D. candidate in the Department of Industrial Engineering and Operations Research at Columbia University supervised by Dr. Rachel Cummings. Her research interests primarily lie at the intersection of data privacy (differential privacy) and statistics with applications in machine learning. In particular, she works on designing privacy-preserving algorithms for machine learning and statistical models, as well as developing obfuscation techniques for online privacy protection.