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dc.contributor.authorLut, Yuliia
dc.date.accessioned2021-03-08T16:43:56Z
dc.date.available2021-03-08T16:43:56Z
dc.date.issued2021-02-26
dc.identifier.urihttp://hdl.handle.net/1853/64351
dc.descriptionPresented online on February 26, 2021 at 12:00 p.m.en_US
dc.descriptionYuliia 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.
dc.descriptionRuntime: 64:24 minutes
dc.description.abstractAccurately 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?en_US
dc.format.extent64:24 minutes
dc.language.isoen_USen_US
dc.publisherGeorgia Institute of Technologyen_US
dc.relation.ispartofseriesCybersecurity Lecture Series;
dc.subjectBrowsing behavioren_US
dc.subjectExperiment designen_US
dc.subjectStatistical analysisen_US
dc.titleRecent Insights from Analysis of Users' Web Browsing Behavioren_US
dc.typeLectureen_US
dc.typeVideoen_US
dc.contributor.corporatenameGeorgia Institute of Technology. Institute for Information Security & Privacyen_US
dc.contributor.corporatenameGeorgia Institute of Technology. School of Industrial and Systems Engineeringen_US
dc.contributor.corporatenameGeorgia Institute of Technology. School of Electrical and Computer Engineeringen_US
dc.contributor.corporatenameColumbia University. Dept. of Industrial Engineering and Operations Researchen_US


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