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Online Advertising under Internet Censorship

Published:30 November 2017Publication History

ABSTRACT

Online advertising plays a critical role in enabling the free Web by allowing publishers to monetize their services. However, the rise in internet censorship events globally poses an economic threat to the advertising ecosystem. This paper studies this interplay and presents Advention, a system that provides censorship circumvention while serving relevant ads. Advention leverages the observation that ad systems are usually hosted on domains that are different from the publisher domains and are almost always uncensored. Taking cue from this, Advention fetches ads via the direct, uncensored, channel between users and the ad system. Preliminary results show that Advention not only offers high ad relevance compared to other popular relay-based circumvention tools, it also offers smaller page load times.

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References

  1. 2016. U.S. digital ad spending to surpass TV this year: Digital will represent 37% of U.S. total media ad spending. In eMarketer. https://www.emarketer.com/Article/US-Digital-Ad-Spending-Surpass-TV-this-Year/1014469Google ScholarGoogle Scholar
  2. ABP. 2017. Adblock Plus. https://adblockplus.org/Google ScholarGoogle Scholar
  3. Alexa. 2017. The top 500 sites on the web. http://www.alexa.com/topsitesGoogle ScholarGoogle Scholar
  4. An OpenX whitepaper. 2013. Ad Networks vs. Ad Exchanges: How They Stack Up. (2013). http://openx.com/whitepapers/.Google ScholarGoogle Scholar
  5. An OpenX whitepaper. 2015. Ad Exchanges Are (not) All The Same. (2015). http://openx.com/whitepapers/.Google ScholarGoogle Scholar
  6. AudienceProject. 2017. Smarter ads can help curb ad blocking. https://tinyurl.com/yb6vvz8uGoogle ScholarGoogle Scholar
  7. Muhammad Ahmad Bashir, Sajjad Arshad, William Robertson, and Christo Wilson. 2016. Tracing Information Flows Between Ad Exchanges Using Retargeted Ads. In USENIX Security Symposium.Google ScholarGoogle Scholar
  8. Juan Miguel Carrascosa, Jakub Mikians, Ruben Cuevas, Vijay Erramilli, and Nikolaos Laoutaris. 2015. I always feel like somebody's watching me. In ACM CoNEXT.Google ScholarGoogle Scholar
  9. Farah Chanchary and Sonia Chiasson. 2015. User Perceptions of Sharing, Advertising, and Tracking. In SOUPS.Google ScholarGoogle Scholar
  10. Roger Dingledine, Nick Mathewson, and Paul Syverson. 2004. Tor: The Second-generation Onion Router. In USENIX Security Symposium. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. DoubleClick. 2017. Websites using DoubleClick.Net. http://trends.builtwith.com/websitelist/DoubleClick.NetGoogle ScholarGoogle Scholar
  12. S. Englehardt and A. Narayanan. 2016. Online Tracking. In ACM CCS.Google ScholarGoogle Scholar
  13. A. Filasto and J. Appelbaum. 2012. OONI: Open Observatory of Network Interference. In FOCI.Google ScholarGoogle Scholar
  14. Electronic Frontier Foundation. 2017. HTTPS Everywhere. https://www.eff.org/https-everywhereGoogle ScholarGoogle Scholar
  15. Phillipa Gill, Masashi Crete-Nishihata, Jakub Dalek, Sharon Goldberg, Adam Senft, and Greg Wiseman. 2015. Characterizing Web Censorship Worldwide: Another Look at the OpenNet Initiative Data. ACM Trans. Web 9, 1, Article 4 (Jan. 2015), 4:1--4:29 pages. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Phillipa Gill, Vijay Erramilli, Augustin Chaintreau, Balachander Krishnamurthy, Konstantina Papagiannaki, and Pablo Rodriguez. 2013. Follow the Money: Understanding Economics of Online Aggregation and Advertising. In ACM IMC. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. Google. 2017. Ad targeting by language. https://support.google.com/adsense/answer/2753586Google ScholarGoogle Scholar
  18. Saikat Guha, Bin Cheng, and Paul Francis. 2010. Challenges in Measuring Online Advertising Systems. In ACM IMC. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. Saikat Guha, Bin Cheng, and P. Francis. 2011. Privad: Practical Privacy in Online Advertising. In NSDI. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. HubSpot. 2017. Why People Block Ads (And What It Means for Marketers and Advertisers). https://research.hubspot.com/reports/why-people-block-ads-and-what-it-means-for-marketers-and-advertisersGoogle ScholarGoogle Scholar
  21. Marc Juarez, Sadia Afroz, Gunes Acar, Claudia Diaz, and Rachel Greenstadt. 2014. A Critical Evaluation of Website Fingerprinting Attacks. In ACM CCS. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. Lantern. 2017. Lantern: Faster than a VPN. https://getlantern.org/Google ScholarGoogle Scholar
  23. Bin Liu, Anmol Sheth, Udi Weinsberg, Jaideep Chandrashekar, and Ramesh Govindan. 2013. AdReveal: improving transparency into online targeted advertising. In ACM HotNets. Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. Marco Lui and Timothy Baldwin. 2012. langid.py: An off-the-shelf language identification tool. In ACL. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. Muhammad Haris Mughees, Zhiyun Qian, and Zubair Shafiq. 2017. Detecting Anti Ad-blockers in the Wild. In PETS.Google ScholarGoogle Scholar
  26. Aqib Nisar, Aqsa Kashaf, Zartash Afzal Uzmi, and Ihsan Ayyub Qazi. 2015. A Case for Marrying Censorship Measurements with Circumvention. In ACM HotNets. Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. PAC. 2017. Proxy Auto-Config. https://en.wikipedia.org/wiki/Proxy_auto-configGoogle ScholarGoogle Scholar
  28. Fotios Papaodyssefs, Costas Iordanou, Jeremy Blackburn, Konstantina Papagiannaki, and Nikolaos Laoutaris. 2015. Web Identity Translator. In ACM HotNets.Google ScholarGoogle Scholar
  29. Pubmatic. 2017. Websites using Pubmatic. http://trends.builtwith.com/websitelist/PubmaticGoogle ScholarGoogle Scholar
  30. Franziska Roesner, Tadayoshi Kohno, and David Wetherall. 2012. Detecting and defending against third-party tracking on the web. In NSDI. Google ScholarGoogle ScholarDigital LibraryDigital Library
  31. Selenium. 2017. Selenium: Automating Web Browsers. http://www.seleniumhq.org/aboutGoogle ScholarGoogle Scholar
  32. Hotspot Shield. 2017. Hotspot Shield. https://www.hotspotshield.com/Google ScholarGoogle Scholar
  33. Vincent Toubiana, Arvind Narayanan, Dan Boneh, Helen Nissenbaum, and Solon Barocas. 2010. Adnostic: Privacy Preserving Targeted Advertising. In NDSS.Google ScholarGoogle Scholar
  34. uProxy. 2017. uProxy: Your private access to the open internet. https://www.uproxy.org/Google ScholarGoogle Scholar
  35. Blase Ur, Pedro Giovanni Leon, Lorrie Faith Cranor, Richard Shay, and Yang Wang. 2012. Smart, Useful, Scary, Creepy: Perceptions of Online Behavioral Advertising. In SOUPS. Google ScholarGoogle ScholarDigital LibraryDigital Library

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          • Published in

            cover image ACM Conferences
            HotNets '17: Proceedings of the 16th ACM Workshop on Hot Topics in Networks
            November 2017
            206 pages
            ISBN:9781450355698
            DOI:10.1145/3152434

            Copyright © 2017 ACM

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            Publication History

            • Published: 30 November 2017

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            HotNets '17 Paper Acceptance Rate28of124submissions,23%Overall Acceptance Rate110of460submissions,24%
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