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Missed by Filter Lists: Detecting Unknown Third-Party Trackers with Invisible Pixels

Authors
  • fouad, Imane
  • Bielova, Nataliia
  • Legout, Arnaud
  • Sarafijanovic-Djukic, Natasa
Type
Published Article
Journal
Proceedings on Privacy Enhancing Technologies
Publisher
Sciendo
Publication Date
Apr 01, 2020
Volume
2020
Issue
2
Pages
499–518
Identifiers
DOI: 10.2478/popets-2020-0038
Source
De Gruyter
Keywords
License
Green

Abstract

Web tracking has been extensively studied over the last decade. To detect tracking, previous studies and user tools rely on filter lists. However, it has been shown that filter lists miss trackers. In this paper, we propose an alternative method to detect trackers inspired by analyzing behavior of invisible pixels. By crawling 84,658 webpages from 8,744 domains, we detect that third-party invisible pixels are widely deployed: they are present on more than 94.51% of domains and constitute 35.66% of all third-party images. We propose a fine-grained behavioral classification of tracking based on the analysis of invisible pixels. We use this classification to detect new categories of tracking and uncover new collaborations between domains on the full dataset of 4, 216, 454 third-party requests. We demonstrate that two popular methods to detect tracking, based on EasyList&EasyPrivacy and on Disconnect lists respectively miss 25.22% and 30.34% of the trackers that we detect. Moreover, we find that if we combine all three lists, 379, 245 requests originated from 8,744 domains still track users on 68.70% of websites.

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