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Who's Most Targeted and Does My New Adblocker Really Help: A Profile-based Evaluation of Personalized Advertising
Linköping University, Department of Computer and Information Science, Database and information techniques. Linköping University, Faculty of Science & Engineering.
Linköping University, Department of Computer and Information Science, Database and information techniques. Linköping University, Faculty of Science & Engineering.
Linköping University, Department of Computer and Information Science, Database and information techniques. Linköping University, Faculty of Science & Engineering.
Linköping University, Department of Computer and Information Science, Database and information techniques. Linköping University, Faculty of Science & Engineering.
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2021 (English)In: Proc. ACM CCS Workshop on Privacy in the Electronic Society (ACM WPES @CCS), ACM Digital Library, 2021Conference paper, Published paper (Refereed)
Abstract [en]

There is limited prior work studying how the ad personalization experienced by different users is impacted by the use of adblockers, geographic location, the user's persona, or what browser they use. To address this void, this paper presents a novel profile-based evaluation of the personalization experienced by carefully crafted user profiles. Our evaluation framework impersonates different users and captures how the personalization changes over time, how it changes when adding or removing an extension, and perhaps most importantly how the results differ depending on the profile's persona (e.g., interest, occupation, age, gender), geographic location (US East, US West, UK), what browser extension they use (none, AdBlock, AdBlock Plus, Ghostery, CatBlock), what browser they use (Chrome, Firefox), and whether they are logged in to their Google account. By comparing and contrasting observed differences we provide insights that help explain why some user groups may feel more targeted than others and why some people may feel even more targeted after having turned on their adblocker.  

Place, publisher, year, edition, pages
ACM Digital Library, 2021.
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:liu:diva-180863DOI: 10.1145/3463676.3485617ISBN: 9781450385275 (print)OAI: oai:DiVA.org:liu-180863DiVA, id: diva2:1609146
Conference
2021 ACM SIGSAC Conference on Computer and Communications Security, Virtual Event Republic of Korea, 15 November 2021
Funder
Wallenberg AI, Autonomous Systems and Software Program (WASP)Available from: 2021-11-06 Created: 2021-11-06 Last updated: 2023-04-03Bibliographically approved

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Minh Ha, LeCarlsson, Niklas

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Bertmar, SofiaGerhardsen, JohannaEkblad, AliceHöglund, AnnaMineur, JuliaOknegard Enavall, IsabellMinh Ha, LeCarlsson, Niklas
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Database and information techniquesFaculty of Science & Engineering
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Citation style
  • apa
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