skip to main content
10.1145/3091478.3091514acmconferencesArticle/Chapter ViewAbstractPublication PageswebsciConference Proceedingsconference-collections
short-paper

Ad-blocking: A Study on Performance, Privacy and Counter-measures

Published:25 June 2017Publication History

ABSTRACT

Many internet ventures rely on advertising for their revenue. However, users feel discontent by the presence of ads on the websites they visit, as the data-size of ads is often comparable to that of the actual content. This has an impact not only on the loading time of webpages, but also on the internet bill of the user in some cases. In absence of a mutually-agreed procedure for opting out of advertisements, many users resort to ad-blocking browser-extensions.

In this work, we study the performance of popular ad-blockers on a large set of news websites. Moreover, we investigate the benefits of ad-blockers on user privacy as well as the mechanisms used by websites to counter them. Finally, we explore the traffic overhead due to the ad-blockers themselves.

References

  1. Paul Barford, Igor Canadi, Darja Krushevskaja, Qiang Ma, and S. Muthukrishnan. 2014. Adscape: Harvesting and analyzing online display ads. In WWW. 597--608. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. David S. Evans. 2008. The economics of the online advertising industry. Review of network economics 7, 3 (2008).Google ScholarGoogle Scholar
  3. Marjan Falahrastegar, Hamed Haddadi, Steve Uhlig, and Richard Mortier. 2014. Anatomy of the third-party web tracking ecosystem. arXiv preprint arXiv:1409.1066 (2014).Google ScholarGoogle Scholar
  4. Matthew Malloy, Mark McNamara, Aaron Cahn, and Paul Barford. 2016. Ad Blockers: Global Prevalence and Impact. In Proceedings of the 2016 ACM on Internet Measurement Conference. 119--125. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Rishab Nithyanand and others. 2016. Ad-Blocking and Counter Blocking: A Slice of the Arms Race. arXiv:1605.05077 (2016).Google ScholarGoogle Scholar
  6. PageFair. 2015. The 2015 Ad Blocking Report. https://blog.pagefair.com/2015/adblocking-report/ (2015).Google ScholarGoogle Scholar
  7. PageFair. 2016. The 2016 Mobile Ad Blocking Report. https://pagefair.com/blog/2016/mobile-adblocking-report/ (2016).Google ScholarGoogle Scholar
  8. Enric Pujol, Oliver Hohlfeld, and Anja Feldmann. 2015. Annoyed Users: Ads and Ad-Block Usage in the Wild. In IMC. 93--106. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Sebastian Schelter and Jérôme Kunegis. 2016. On the Ubiquity of Web Tracking: Insights from a Billion-Page Web Crawl. arXiv preprint arXiv:1607.07403 (2016).Google ScholarGoogle Scholar
  10. Robert J. Walls, Eric D. Kilmer, Nathaniel Lageman, and Patrick D. McDaniel. 2015. Measuring the Impact and Perception of Acceptable Advertisements. In IMC. 107--120. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Craig E. Wills and Doruk C. Uzunoglu. 2016. What Ad Blockers Are (and Are Not) Doing. In HotWeb. 72--77.Google ScholarGoogle Scholar
  12. Zhonghao Yu, Sam Macbeth, Konark Modi, and Josep M. Pujol. 2016. Tracking the Trackers. In WWW. 121--132. Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. Ad-blocking: A Study on Performance, Privacy and Counter-measures

      Recommendations

      Comments

      Login options

      Check if you have access through your login credentials or your institution to get full access on this article.

      Sign in
      • Published in

        cover image ACM Conferences
        WebSci '17: Proceedings of the 2017 ACM on Web Science Conference
        June 2017
        438 pages
        ISBN:9781450348966
        DOI:10.1145/3091478

        Copyright © 2017 ACM

        Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

        Publisher

        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 25 June 2017

        Permissions

        Request permissions about this article.

        Request Permissions

        Check for updates

        Qualifiers

        • short-paper

        Acceptance Rates

        WebSci '17 Paper Acceptance Rate30of85submissions,35%Overall Acceptance Rate218of875submissions,25%

      PDF Format

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader