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Computer Science > Cryptography and Security

arXiv:2208.12370 (cs)
[Submitted on 25 Aug 2022 (v1), last revised 27 Nov 2023 (this version, v5)]

Title:COOKIEGRAPH: Understanding and Detecting First-Party Tracking Cookies

Authors:Shaoor Munir, Sandra Siby, Umar Iqbal, Steven Englehardt, Zubair Shafiq, Carmela Troncoso
View a PDF of the paper titled COOKIEGRAPH: Understanding and Detecting First-Party Tracking Cookies, by Shaoor Munir and 5 other authors
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Abstract:As third-party cookie blocking is becoming the norm in browsers, advertisers and trackers have started to use first-party cookies for tracking. We conduct a differential measurement study on 10K websites with third-party cookies allowed and blocked. This study reveals that first-party cookies are used to store and exfiltrate identifiers to known trackers even when third-party cookies are blocked.
As opposed to third-party cookie blocking, outright first-party cookie blocking is not practical because it would result in major functionality breakage. We propose CookieGraph, a machine learning-based approach that can accurately and robustly detect first-party tracking cookies. CookieGraph detects first-party tracking cookies with 90.20% accuracy, outperforming the state-of-the-art CookieBlock approach by 17.75%. We show that CookieGraph is fully robust against cookie name manipulation while CookieBlock's acuracy drops by 15.68%. While blocking all first-party cookies results in major breakage on 32% of the sites with SSO logins, and CookieBlock reduces it to 10%, we show that CookieGraph does not cause any major breakage on these sites.
Our deployment of CookieGraph shows that first-party tracking cookies are used on 93.43% of the 10K websites. We also find that first-party tracking cookies are set by fingerprinting scripts. The most prevalent first-party tracking cookies are set by major advertising entities such as Google, Facebook, and TikTok.
Subjects: Cryptography and Security (cs.CR)
Cite as: arXiv:2208.12370 [cs.CR]
  (or arXiv:2208.12370v5 [cs.CR] for this version)
  https://doi.org/10.48550/arXiv.2208.12370
arXiv-issued DOI via DataCite

Submission history

From: Shaoor Munir [view email]
[v1] Thu, 25 Aug 2022 22:56:31 UTC (1,751 KB)
[v2] Fri, 2 Sep 2022 06:11:48 UTC (1,751 KB)
[v3] Wed, 1 Feb 2023 09:02:11 UTC (1,952 KB)
[v4] Mon, 25 Sep 2023 03:13:18 UTC (576 KB)
[v5] Mon, 27 Nov 2023 10:19:03 UTC (575 KB)
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