{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T16:19:36Z","timestamp":1783009176676,"version":"3.54.5"},"reference-count":75,"publisher":"Privacy Enhancing Technologies Symposium Advisory Board","issue":"4","license":[{"start":{"date-parts":[[2018,8,29]],"date-time":"2018-08-29T00:00:00Z","timestamp":1535500800000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/3.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018,10,1]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Advertising and Analytics (A&amp;A) companies have started collaborating more closely with one another due to the shift in the online advertising industry towards Real Time Bidding (RTB). One natural way to understand how user tracking data moves through this interconnected advertising ecosystem is by modeling it as a graph. In this paper, we introduce a novel graph representation, called an<jats:italic>Inclusion<\/jats:italic>graph, to model the impact of RTB on the diffusion of user tracking data in the advertising ecosystem. Through simulations on the<jats:italic>Inclusion<\/jats:italic>graph, we provide upper and lower estimates on the tracking information observed by A&amp;A companies. We find that 52 A&amp;A companies observe at least 91% of an average user\u2019s browsing history under reasonable assumptions about information sharing within RTB auctions. We also evaluate the effectiveness of blocking strategies (e.g., AdBlock Plus), and find that major A&amp;A companies still observe 40\u201390% of user impressions, depending on the blocking strategy.<\/jats:p>","DOI":"10.1515\/popets-2018-0033","type":"journal-article","created":{"date-parts":[[2018,8,31]],"date-time":"2018-08-31T09:30:30Z","timestamp":1535707830000},"page":"85-103","source":"Crossref","is-referenced-by-count":41,"title":["Diffusion of User Tracking Data in the Online Advertising Ecosystem"],"prefix":"10.56553","volume":"2018","author":[{"given":"Muhammad Ahmad","family":"Bashir","sequence":"first","affiliation":[{"name":"Northeastern University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christo","family":"Wilson","sequence":"additional","affiliation":[{"name":"Northeastern University"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"35752","published-online":{"date-parts":[[2018,8,29]]},"reference":[{"key":"2021040803473653012_j_popets-2018-0033_ref_001_w2aab3b7b6b1b6b1ab1ab1Aa","doi-asserted-by":"crossref","unstructured":"[1] Gunes Acar, Christian Eubank, Steven Englehardt, Marc Juarez, Arvind Narayanan, and Claudia Diaz. 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