research-article

Open access

CookieGraph: Understanding and Detecting First-Party Tracking Cookies

Authors:

Steven Englehardt,

Carmela TroncosoAuthors Info & Claims

CCS '23: Proceedings of the 2023 ACM SIGSAC Conference on Computer and Communications Security

Pages 3490 - 3504

https://doi.org/10.1145/3576915.3616586

Published: 21 November 2023 Publication History

Abstract

As third-party cookie blocking is becoming the norm in mainstream web browsers, advertisers and trackers have started to use first-party cookies for tracking. To understand this phenomenon, we conduct a differential measurement study with versus without third-party cookies. We find 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, first-party cookie blocking is not practical because it would result in major breakage of website functionality. We propose CookieGraph, a machine learning-based approach that can accurately and robustly detect and block first-party tracking cookies. CookieGraph detects first-party tracking cookies with 90.18% accuracy, outperforming the state-of-the-art CookieBlock by 17.31%. We show that CookieGraph is robust against cookie name manipulation, while CookieBlock's accuracy drops by 15.87%. 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 89.86% of the top-million websites. We find that 96.61% of these first-party tracking cookies are in fact ghostwritten by third-party scripts embedded in the first-party context. We also find evidence of first-party tracking cookies being set by fingerprinting scripts. The most prevalent first-party tracking cookies are set by major advertising entities such as Google, Facebook, and TikTok.

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Cited By

Sim KHeo HCho H(2024)Combating Web Tracking: Analyzing Web Tracking Technologies for User PrivacyFuture Internet10.3390/fi1610036316:10(363)Online publication date: 5-Oct-2024
https://doi.org/10.3390/fi16100363
Hajj Chehade SSiby STroncoso C(2024)SINBAD: Saliency-informed detection of breakage caused by ad blocking2024 IEEE Symposium on Security and Privacy (SP)10.1109/SP54263.2024.00199(258-276)Online publication date: 19-May-2024
https://doi.org/10.1109/SP54263.2024.00199
Bou Abdo JZeadally S(2024)Disposable identitiesJournal of Information Security and Applications10.1016/j.jisa.2024.10382184:COnline publication date: 25-Sep-2024
https://dl.acm.org/doi/10.1016/j.jisa.2024.103821
Show More Cited By

Index Terms

CookieGraph: Understanding and Detecting First-Party Tracking Cookies
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Classification and regression trees
2. Security and privacy
  1. Human and societal aspects of security and privacy
    1. Privacy protections
    2. Usability in security and privacy
  2. Software and application security
    1. Domain-specific security and privacy architectures

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Published In

cover image ACM Conferences

CCS '23: Proceedings of the 2023 ACM SIGSAC Conference on Computer and Communications Security

November 2023

3722 pages

ISBN:9798400700507

DOI:10.1145/3576915

General Chairs:
Weizhi Meng
Technical University of Denmark
,
Christian D. Jensen
Technical University of Denmark
,
Program Chairs:
Cas Cremers
CISPA Helmholtz Center for Information Security
,
Engin Kirda
Khoury College of Computer Sciences

Copyright © 2023 Owner/Author.

This work is licensed under a Creative Commons Attribution International 4.0 License.

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Published: 21 November 2023

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CCS '23: ACM SIGSAC Conference on Computer and Communications Security

November 26 - 30, 2023

Copenhagen, Denmark

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Overall Acceptance Rate 1,261 of 6,999 submissions, 18%

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Cited By

Sim KHeo HCho H(2024)Combating Web Tracking: Analyzing Web Tracking Technologies for User PrivacyFuture Internet10.3390/fi1610036316:10(363)Online publication date: 5-Oct-2024
https://doi.org/10.3390/fi16100363
Hajj Chehade SSiby STroncoso C(2024)SINBAD: Saliency-informed detection of breakage caused by ad blocking2024 IEEE Symposium on Security and Privacy (SP)10.1109/SP54263.2024.00199(258-276)Online publication date: 19-May-2024
https://doi.org/10.1109/SP54263.2024.00199
Bou Abdo JZeadally S(2024)Disposable identitiesJournal of Information Security and Applications10.1016/j.jisa.2024.10382184:COnline publication date: 25-Sep-2024
https://dl.acm.org/doi/10.1016/j.jisa.2024.103821
Ahmad ZCalzavara SCasarin SStock B(2024)Information flow control for comparative privacy analysesInternational Journal of Information Security10.1007/s10207-024-00886-023:5(3199-3216)Online publication date: 14-Jul-2024
https://dl.acm.org/doi/10.1007/s10207-024-00886-0
Alharbi JAlbesher AWahsheh H(2023)An Empirical Analysis of E-Governments’ Cookie Interfaces in 50 CountriesSustainability10.3390/su1502123115:2(1231)Online publication date: 9-Jan-2023
https://doi.org/10.3390/su15021231

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