Tracking the Pixels: Detecting Web Trackers via Analyzing Invisible Pixels
Friday, January 18, 2019 at 15:00, Fermat F102, by Imane Fouad (Inria INDES)
Web tracking has been extensively studied over the last decade. To detect tracking, most of the research studies and user tools rely on consumer protection lists. However, there was always a suspicion that lists miss unknown trackers. In this paper, we propose an alternative solution to detect trackers by analyzing behavior of invisible pixels that are perfect suspects for tracking. By crawling 829,349 webpages, we detect that third-party invisible pixels are widely deployed: they are present on more than 83% of domains and constitute 37.22% of all third-party images. We then propose a fine-grained classification of tracking based on the analysis of invisible pixels and use this classification to detect new categories of tracking and uncover new collaborations between domains on the full dataset of 34,952,217 third-party requests. We demonstrate that two blocking strategies — based on EasyList&EasyPrivacy and on Disconnect lists — each miss 22% of the trackers that we detect. Moreover, we find that if we combine both strategies, 238,439 requests (11%) originated from 7,773 domains that still track users on 5,098 websites.
Friday, October 26, 2018 at 15:00, Fermat F102, by Manuel Serrano (Inria INDES)
JAMScript — A Programming Framework for Cloud of Things
Friday, October 5, 2018 at 10:30, Fermat F321, by Jayanth Krishnamurthy (Inria INDES)