Wide-AdGraph: Detecting Ad Trackers with a Wide Dependency Chain Graph

Amir Hossein Kargaran, Mohammad Sadegh Akhondzadeh, Mohammad Reza Heidarpour, Mohammad Hossein Manshaei, Kave Salamatian, Masoud Nejad Sattary

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Websites use third-party ads and tracking services to deliver targeted ads and collect information about users that visit them. These services put users' privacy at risk, and that is why users' demand for blocking these services is growing. Most of the blocking solutions rely on crowd-sourced filter lists manually maintained by a large community of users. In this work, we seek to simplify the update of these filter lists by combining different websites through a large-scale graph connecting all resource requests made over a large set of sites. The features of this graph are extracted and used to train a machine learning algorithm with the aim of detecting ads and tracking resources. As our approach combines different information sources, it is more robust toward evasion techniques that use obfuscation or changing the usage patterns. We evaluate our work over the Alexa top-10K websites and find its accuracy to be 96.1% biased and 90.9% unbiased with high precision and recall. It can also block new ads and tracking services, which would necessitate being blocked by further crowd-sourced existing filter lists. Moreover, the approach followed in this paper sheds light on the ecosystem of third-party tracking and advertising.

Original languageEnglish (US)
Title of host publicationWebSci 2021 - Proceedings of the 13th ACM Web Science Conference
PublisherAssociation for Computing Machinery
Pages253-261
Number of pages9
ISBN (Electronic)9781450383301
DOIs
StatePublished - Jun 21 2021
Externally publishedYes
Event13th ACM Web Science Conference, WebSci 2021 - Virtual, Online, United Kingdom
Duration: Jun 21 2021Jun 25 2021

Publication series

NameACM International Conference Proceeding Series

Conference

Conference13th ACM Web Science Conference, WebSci 2021
Country/TerritoryUnited Kingdom
CityVirtual, Online
Period6/21/216/25/21

Keywords

  • Tracking
  • ad blocking
  • crowdsource
  • data privacy
  • filter lists

ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications

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