Pith. sign in

REVIEW 2 major objections 2 minor 239 references

SoK: After Decades of Web Tracker Detection, What's Next?

T0 review · 2 major / 2 minor · reviewed 2026-05-08 · grok-4.3

Pith's one-line read A systematic review of decades of web tracker detection research yields a new taxonomy, trend analysis, and list of open gaps.

desk verdict This SoK gives a practical taxonomy of web tracker detectors plus trend observations and reproducibility flags, but the selection of the 59 studies from 832 papers is described too lightly to fully back the comprehensiveness claim. read the letter →

arxiv 2605.02982 v1 submitted 2026-05-04 cs.CR

classification cs.CR
keywords webtrackingtrackerdetectionfilterlistssystematicreviewSoKprivacyreproducibilitytaxonomy
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper performs the largest meta-review to date of web tracker detection by screening 832 papers down to 59 primary studies. It organizes existing detectors into a taxonomy, tracks how methods have evolved over time, flags reproducibility problems through a limited re-implementation check, and lists concrete research gaps plus practical recommendations. A sympathetic reader would care because everyday web users rely on imperfect filter lists and blockers whose shortcomings this work aims to overcome. If the synthesis holds, future detector design can avoid repeating known limitations and target the uncovered challenges more directly.

What carries the argument

The taxonomy of web tracker detectors constructed from the 59 primary studies, which classifies approaches and supports trend evaluation and gap identification.

What would settle it

Discovery of a large body of tracker-detection papers or methods that the inclusion criteria excluded, or a successful large-scale re-implementation showing that the reported trends and gaps are artifacts of the selected sample.

Watch

Extended reading notes

Core claim

By conducting a systematic literature review of web tracker detection literature and synthesizing 59 primary studies, the authors establish a taxonomy of detection approaches, document observable trends in the field, identify open research gaps, issue recommendations for future work, and demonstrate through a reproducibility assessment that many prior studies contain validity issues that undermine their conclusions.

Load-bearing premise

The inclusion and exclusion criteria applied to the 832-paper corpus produced an unbiased and representative set of 59 primary studies that fully capture the state of web tracker detection research.

Editorial extensions

If this is right

  • Detector development should shift focus to the open gaps such as advanced obfuscation and dynamic tracking techniques.
  • Reproducibility practices must improve because the limited re-evaluation already exposed validity problems in earlier work.
  • Future research can use the proposed taxonomy as a common reference frame to compare new detectors against prior ones.
  • Recommendations for evaluation metrics and data sets can reduce duplication and increase comparability across studies.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The taxonomy could serve as a practical checklist for developers building or auditing privacy tools such as browser extensions.
  • Addressing the reproducibility issues may require shared test beds or open data sets that later papers could adopt.
  • Connections to adjacent privacy areas like fingerprinting or consent management could be explored using the same systematization approach.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

2 major / 2 minor

Summary. The paper presents an SoK on web tracker detection, claiming to conduct the most comprehensive meta-science study to date. It performs a systematic literature review on a corpus of 832 papers, selecting 59 primary and 16 supplementary studies, from which it derives a taxonomy of detectors, observes and evaluates trends, identifies open research gaps, and provides recommendations for future work. It also includes a limited reproducibility study to assess the validity of prior research.

Significance. If the review corpus is representative, this work offers a valuable synthesis of decades of web tracker detection research, providing a taxonomy and actionable recommendations that could guide future studies in web privacy and security. The limited reproducibility study is a strength, highlighting practical issues in the field and adding empirical grounding to the meta-analysis.

major comments (2)
  1. [§3] §3 (Systematic Review Methodology): The search strategy, databases used, exact search strings (including variants like 'ad detection' or 'privacy measurement'), and precise inclusion/exclusion criteria applied to reduce 832 papers to 59 primary studies are described only at a high level. This detail is load-bearing for the central claim of presenting the 'most comprehensive' study, as incomplete specification prevents assessment of potential selection bias or coverage gaps.
  2. [Reproducibility Study] Reproducibility Study section: The criteria for selecting the subset of studies for the limited reproducibility assessment, along with the exact experimental protocol and metrics used to evaluate validity, are not specified. This undermines the ability to interpret the highlighted 'emerging problems' and assess the study's contribution to evaluating past work.
minor comments (2)
  1. [Taxonomy section] The taxonomy presentation could benefit from a clearer summary table or diagram early in the relevant section to help readers map the 59 studies to categories.
  2. [Trends section] Some trend observations reference specific papers without explicit cross-references to the primary study list or supplementary materials, which would improve traceability.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback and positive assessment of our SoK. We address each major comment below and will revise the manuscript to incorporate additional details where needed.

read point-by-point responses
  1. Referee: [§3] §3 (Systematic Review Methodology): The search strategy, databases used, exact search strings (including variants like 'ad detection' or 'privacy measurement'), and precise inclusion/exclusion criteria applied to reduce 832 papers to 59 primary studies are described only at a high level. This detail is load-bearing for the central claim of presenting the 'most comprehensive' study, as incomplete specification prevents assessment of potential selection bias or coverage gaps.

    Authors: We agree that the methodology in Section 3 is presented at a high level and that greater specificity would strengthen the transparency of our systematic review and support the claim of comprehensiveness. In the revised manuscript we will expand Section 3 (or add an appendix) with the exact search strings employed, the complete list of databases queried, and the full inclusion/exclusion criteria used to arrive at the 59 primary studies. This will enable readers to assess coverage and potential selection bias. revision: yes

  2. Referee: [Reproducibility Study] Reproducibility Study section: The criteria for selecting the subset of studies for the limited reproducibility assessment, along with the exact experimental protocol and metrics used to evaluate validity, are not specified. This undermines the ability to interpret the highlighted 'emerging problems' and assess the study's contribution to evaluating past work.

    Authors: We concur that the Reproducibility Study section would benefit from explicit description of the selection criteria for the subset, the detailed experimental protocol, and the metrics for assessing validity. We will revise this section to include these elements, thereby clarifying how the subset was chosen and how validity was evaluated. This will better contextualize the emerging problems identified and the contribution of the reproducibility analysis. revision: yes

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: synthesis rests on external literature corpus

full rationale

The paper conducts a systematic literature review by applying search and inclusion criteria to an external corpus of 832 papers, yielding 59 primary studies whose content is then used to derive the taxonomy, trends, gaps, and recommendations. No step reduces by construction to the paper's own outputs or fitted parameters; the derivation chain is one-way from reviewed external sources to synthesized claims. No self-definitional loops, fitted-input predictions, or load-bearing self-citations appear in the described process.

Assumptions & free parameters 0 free parameters · 1 assumptions · 0 invented entities

The paper rests on standard systematic-review practices and the existing body of 832 papers; no free parameters, new physical entities, or ad-hoc axioms beyond the review methodology itself.

assumptions (1)
  • domain assumption Standard systematic literature review methodology (search, screening, selection) produces an unbiased representation of the field.
    Invoked in the description of how the 59 primary studies were chosen from 832 papers.

how reviews work

0 comments
Cite this review

Pith. "Pith review of SoK: After Decades of Web Tracker Detection, What's Next?." pith.science (2026). https://pith.science/paper/2605.02982

@misc{pith2026260502982,
  author       = {Pith},
  title        = {Pith review of: SoK: After Decades of Web Tracker Detection, What's Next?},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/2605.02982}},
  note         = {Machine review of arXiv:2605.02982}
}
read the original abstract

Web tracking is an omnipresent phenomenon in today's web, affecting users in their day-to-day lives. Filter lists and blockers were invented to detect trackers and to protect users. Due to limitations of said tools, researchers developed web tracker detectors to replace them. No review constructed a universal perspective and classification of web tracker detectors until now. Past reviews focused either on the field as a whole or on web tracking techniques. In this SoK paper, we present the most comprehensive meta-science study on web tracker detection by systematizing and synthesizing the available knowledge. We conduct a systematic review, resulting in 59 primary and 16 supplementary studies out of a corpus of 832 papers. Based on these findings we suggest a taxonomy, observe and evaluate trends, propose open research gaps, and recommendations with which we aim to lay the foundations for future web tracker detection research. In addition, we conduct a limited reproducibility study to assess the validity of past studies and highlight emerging problems in this field.

Figures

Figures reproduced from arXiv: 2605.02982 by the authors.

Figure 1
Figure 1. Various stakeholders participate throughout the life cycle of a detector. Whereas the left side focuses on detector research and development, the view at source ↗
Figure 2
Figure 2. Adapted PRISMA flow diagram [ view at source ↗
Figure 3
Figure 3. The detector taxonomy comprising ten categories with their view at source ↗

Discussion (0). Continue with ORCID to comment.

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

What do these tags mean?
matches
The paper's claim is directly supported by a theorem in the formal canon.
supports
The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends
The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses
The paper appears to rely on the theorem as machinery.
contradicts
The paper's claim conflicts with a theorem or certificate in the canon.
unclear
Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Reference graph

Works this paper leans on

239 extracted references · 239 canonical work pages

  1. [1]

    The Web Never Forgets: Persistent Tracking Mechanisms in the Wild,

    G. Acar, C. Eubank, S. Englehardt, M. Juarez, A. Narayanan, and C. Diaz, “The Web Never Forgets: Persistent Tracking Mechanisms in the Wild,” inACM Conference on Computer and Communications Security (CCS), 2014, p. 674–689

  2. [2]

    FPDetective: Dusting the Web for Fingerprinters,

    G. Acar, M. Juarez, N. Nikiforakis, C. Diaz, S. G ¨urses, F. Piessens, and B. Preneel, “FPDetective: Dusting the Web for Fingerprinters,” inACM Conference on Computer and Communications Security (CCS), 2013, p. 1129–1140

  3. [3]

    Adblock,

    AdBlock, “Adblock,” [Online]. Available: https://getadblock.com, accessed: Jul. 25, 2025

  4. [4]

    Adblock Plus,

    Adblock Plus, “Adblock Plus,” [Online]. Available: https://adbloc kplus.org/en/, accessed: Jul. 25, 2025

  5. [5]

    Apophanies or Epiphanies? How Crawlers Impact Our Understanding of the Web,

    S. S. Ahmad, M. D. Dar, M. F. Zaffar, N. Vallina-Rodriguez, and R. Nithyanand, “Apophanies or Epiphanies? How Crawlers Impact Our Understanding of the Web,” inThe ACM Web Conference (WWW), 2020, p. 271–280

  6. [6]

    An Empirical Analysis of Web Storage and Its Applications to Web Tracking,

    Z. Ahmad, S. Casarin, and S. Calzavara, “An Empirical Analysis of Web Storage and Its Applications to Web Tracking,”ACM Transac- tions on the Web (TWEB), vol. 18, no. 1, 2023

  7. [7]

    Beyond Cookie Monster Amnesia: Real World Persistent Online Tracking,

    N. M. Al-Fannah, W. Li, and C. J. Mitchell, “Beyond Cookie Monster Amnesia: Real World Persistent Online Tracking,” inInter- national Conference on Information Security (ISC), 2018, pp. 481– 501

  8. [8]

    Alexa Top Sites Ranking,

    Alexa, “Alexa Top Sites Ranking,” [Online]. Available: https://web. archive.org/web/20221126132843/https://support.alexa.com/hc/en-u s/articles/4410503838999, accessed: Aug. 04, 2025

Show all 239 references
  1. [9]

    Errors, Misunder- standings, and Attacks: Analyzing the Crowdsourcing Process of Ad-blocking Systems,

    M. Alrizah, S. Zhu, X. Xing, and G. Wang, “Errors, Misunder- standings, and Attacks: Analyzing the Crowdsourcing Process of Ad-blocking Systems,” inACM Internet Measurement Conference (IMC), 2019, p. 230–244

  2. [10]

    Detecting Web Bugs with Bugnosis: Privacy Advocacy through Education,

    A. Alsaid and D. Martin, “Detecting Web Bugs with Bugnosis: Privacy Advocacy through Education,” inInternational Workshop on Privacy Enhancing Technologies (PET), 2003, pp. 13–26

  3. [11]

    Blocking Tracking JavaScript at the Function Granularity,

    A. H. Amjad, S. Munir, Z. Shafiq, and M. A. Gulzar, “Blocking Tracking JavaScript at the Function Granularity,” inACM Confer- ence on Computer and Communications Security (CCS), 2024, p. 2177–2191

  4. [12]

    TrackerSift: Untangling Mixed Tracking and Functional Web Re- sources,

    A. H. Amjad, D. Saleem, M. A. Gulzar, Z. Shafiq, and F. Zaffar, “TrackerSift: Untangling Mixed Tracking and Functional Web Re- sources,” inACM Internet Measurement Conference (IMC), 2021, pp. 569–576

  5. [13]

    FP-Fed: Privacy-Preserving Federated Detection of Browser Fingerprinting,

    M. Annamalai, I. Bilogrevic, and E. De Cristofaro, “FP-Fed: Privacy-Preserving Federated Detection of Browser Fingerprinting,” inNetwork and Distributed System Security Symposium (NDSS), 2024

  6. [14]

    Safari Browser,

    Apple Inc., “Safari Browser,” [Online]. Available: https://www.appl e.com/de/safari/, accessed: Jul. 30, 2025

  7. [15]

    Available: https://arxiv.org/, accessed: Jul

    arXiv, “arxiv,” [Online]. Available: https://arxiv.org/, accessed: Jul. 27, 2025

  8. [16]

    ACM Digital Library,

    Association for Computing Machinery, “ACM Digital Library,” [On- line]. Available: https://dl.acm.org, accessed: Jul. 27, 2025

  9. [17]

    Artifact Review and Badging – Version 1.0 (not current),

    ——, “Artifact Review and Badging – Version 1.0 (not current),” [Online]. Available: https://www.acm.org/publications/policies/arti fact-review-badging, accessed: Jul. 27, 2025

  10. [18]

    Flash Cookies and Privacy II: Now with HTML5 and ETag Respawning,

    M. D. Ayenson, D. J. Wambach, A. Soltani, N. Good, and C. J. Hoofnagle, “Flash Cookies and Privacy II: Now with HTML5 and ETag Respawning,”SSRN Electronic Journal, 2011

  11. [19]

    FP-Radar: Longitudi- nal Measurement and Early Detection of Browser Fingerprinting,

    P. N. Bahrami, U. Iqbal, and Z. Shafiq, “FP-Radar: Longitudi- nal Measurement and Early Detection of Browser Fingerprinting,” Privacy Enhancing Technologies Symposium (PETS), pp. 557–577, 2022

  12. [20]

    1,500 scientists lift the lid on reproducibility,

    M. Baker, “1,500 scientists lift the lid on reproducibility,”Nature, vol. 533, no. 7604, pp. 452–454, 2016

  13. [21]

    Diffusion of User Tracking Data in the Online Advertising Ecosystem,

    A. Bashir and C. Wilson, “Diffusion of User Tracking Data in the Online Advertising Ecosystem,”Privacy Enhancing Technologies Symposium (PETS), pp. 85–103, 2018

  14. [22]

    A Promising Direction for Web Tracking Countermeasures,

    J. Bau, J. R. Mayer, H. S. Paskov, and J. C. Mitchell, “A Promising Direction for Web Tracking Countermeasures,”Workshop on Web 2.0 Security and Privacy (W2SP), 2013

  15. [23]

    Leveraging Machine Learning to Improve Unwanted Resource Fil- tering,

    S. Bhagavatula, C. Dunn, C. Kanich, M. Gupta, and B. Ziebart, “Leveraging Machine Learning to Improve Unwanted Resource Fil- tering,” inWorkshop on Artificial Intelligent and Security Workshop (AISec), 2014, p. 95–102

  16. [24]

    Blockzilla,

    Blockzilla, “Blockzilla,” [Online]. Available: https://github.com/a nnon79/Blockzilla/blob/master/Blockzilla.txt, accessed: Aug. 13, 2025

  17. [25]

    FP- Tracer: Fine-Grained Browser Fingerprinting Detection via Taint- Tracking and Entropy-Based Thresholds,

    S. Boussaha, L. Hock, M. Bermejo, R. C. Rumin, A. C. Rumin, D. Klein, M. Johns, L. Compagna, D. Antonioli, and T. Barber, “FP- Tracer: Fine-Grained Browser Fingerprinting Detection via Taint- Tracking and Entropy-Based Thresholds,”Privacy Enhancing Tech- nologies Symposium (PE...

  18. [26]

    Fingerprinting defenses 2.0,

    Brave Privacy Team, “Fingerprinting defenses 2.0,” [Online]. Avail- able: https://brave.com/privacy-updates/4-fingerprinting-defenses-2 .0/, accessed: Jul. 30, 2025

  19. [27]

    What Manifest V3 means for Brave Shields and the use of extensions in the Brave browser,

    ——, “What Manifest V3 means for Brave Shields and the use of extensions in the Brave browser,” [Online]. Available: https://brave. com/blog/brave-shields-manifest-v3/, accessed: Aug. 14, 2025

  20. [28]

    Brave Browser,

    Brave Software Inc., “Brave Browser,” [Online]. Available: https: //brave.com, accessed: Jul. 25, 2025

  21. [29]

    A Survey on Web Tracking: Mechanisms, Implications, and Defenses,

    T. Bujlow, V . Carela-Espa ˜nol, J. Sol ´e-Pareta, and P. Barlet-Ros, “A Survey on Web Tracking: Mechanisms, Implications, and Defenses,” Proceedings of the IEEE, vol. 105, no. 8, pp. 1476–1510, 2017

  22. [30]

    byte-learn,

    byte-learn, “byte-learn,” [Online]. Available: https://github.com/byt e-learn/byte-learn, accessed: Nov. 14, 2025

  23. [31]

    RF Data,

    Byte-learn, “RF Data,” [Online]. Available: https://drive.google.com /file/d/1LoddVxdk7v30CClMx3QoEyfZuGt8aQ-q/view?usp=share link, accessed: Nov. 14, 2025

  24. [32]

    AST-GNN: A Graph Neural Network for Web Tracking Detection,

    E. Campeny-Roig, I. Castell-Uroz, and P. Barlet-Ros, “AST-GNN: A Graph Neural Network for Web Tracking Detection,” inGraph Neural Networking Workshop (GNNet), 2024, p. 27–32

  25. [33]

    (Cross-)Browser Fingerprinting via OS and Hardware Level Features,

    Y . Cao, S. Li, and E. Wijmans, “(Cross-)Browser Fingerprinting via OS and Hardware Level Features,” inNetwork and Distributed System Security Symposium (NDSS), 2017

  26. [34]

    OmniCrawl: Comprehensive Measurement of Web Tracking With Real Desktop and Mobile Browsers,

    D. Cassel, S.-C. Lin, A. Buraggina, W. Wang, A. Zhang, L. Bauer, H.-C. Hsiao, L. Jia, and T. Libert, “OmniCrawl: Comprehensive Measurement of Web Tracking With Real Desktop and Mobile Browsers,”Privacy Enhancing Technologies Symposium (PETS), pp. 227–252, 2022

  27. [35]

    Early detection of new web tracking methods across 1.5 million sites,

    I. Castell-Uroz, ´O. S. de Mingo, and P. Barlet-Ros, “Early detection of new web tracking methods across 1.5 million sites,”Computer Communications, vol. 210, pp. 273–284, 2023

  28. [36]

    ASTrack: Automatic Detection and Removal of Web Tracking Code with Minimal Func- tionality Loss,

    I. Castell-Uroz, K. Fukuda, and P. Barlet-Ros, “ASTrack: Automatic Detection and Removal of Web Tracking Code with Minimal Func- tionality Loss,” inIEEE Conference on Computer Communications (INFOCOM), 2023, pp. 1–10

  29. [37]

    URL-based Web Tracking Detection Using Deep Learning,

    I. Castell-Uroz, T. Poissonnier, P. Manneback, and P. Barlet-Ros, “URL-based Web Tracking Detection Using Deep Learning,” in International Conference on Network and Service Management (CNSM), 2020, pp. 1–5

  30. [38]

    TrackSign: Guided Web Tracking Discovery,

    I. Castell-Uroz, J. Sole-Pareta, and P. Barlet-Ros, “TrackSign: Guided Web Tracking Discovery,” inIEEE Conference on Computer Communications (INFOCOM), 2021, pp. 1–10

  31. [39]

    Hide and Seek: Revisiting DNS-based User Tracking,

    D. Chang, J. Q. Chen, Z. Li, and X. Li, “Hide and Seek: Revisiting DNS-based User Tracking,” inEuropean Symposium on Security and Privacy (EuroS&P), 2022, pp. 188–205

  32. [40]

    Detecting filter list evasion with event-loop-turn granularity javascript signatures,

    Q. Chen, P. Snyder, B. Livshits, and A. Kapravelos, “Detecting filter list evasion with event-loop-turn granularity javascript signatures,” in IEEE Symposium on Security and Privacy (S&P), 2021, pp. 1715– 1729

  33. [41]

    Using Function Call Sequence for Browser Fingerprint- ing Detection,

    D. Cheng, “Using Function Call Sequence for Browser Fingerprint- ing Detection,” inInternational Conference on Computer Science and Management Technology (ICCSMT), 2022, pp. 104–109

  34. [42]

    Threats to Online Advertising and Countermeasures: A Technical Survey,

    M. Y .-K. Chua, G. O. M. Yee, Y . X. Gu, and C.-H. Lung, “Threats to Online Advertising and Countermeasures: A Technical Survey,” Digital Threats, vol. 1, no. 2, 2020

  35. [43]

    Narrowing the focus and broadening horizons: Com- plementary roles for systematic and nonsystematic reviews,

    D. A. Cook, “Narrowing the focus and broadening horizons: Com- plementary roles for systematic and nonsystematic reviews,”Ad- vances in Health Sciences Education, vol. 13, no. 4, pp. 391–395, 2008

  36. [44]

    Inferring Tracker-Advertiser Relationships in the Online Advertising Ecosystem using Header Bidding,

    J. Cook, R. Nithyanand, and Z. Shafiq, “Inferring Tracker-Advertiser Relationships in the Online Advertising Ecosystem using Header Bidding,”Privacy Enhancing Technologies Symposium (PETS), pp. 65–82, 2020

  37. [45]

    Hybrid and lightweight detection of third party tracking: Design, implementation, and evaluation,

    F. Cozza, A. Guarino, F. Isernia, D. Malandrino, A. Rapuano, R. Schiavone, and R. Zaccagnino, “Hybrid and lightweight detection of third party tracking: Design, implementation, and evaluation,” Computer Networks, vol. 167, p. 106993, 2020

  38. [46]

    A machine learning approach for detecting CNAME cloaking-based tracking on the Web,

    H. Dao and K. Fukuda, “A machine learning approach for detecting CNAME cloaking-based tracking on the Web,” inIEEE Global Communications Conference (GLOBECOM), 2020, pp. 1–6

  39. [47]

    Cname cloaking-based track- ing on the web: Characterization, detection, and protection,

    H. Dao, J. Mazel, and K. Fukuda, “Cname cloaking-based track- ing on the web: Characterization, detection, and protection,”IEEE Transactions on Network and Service Management, vol. 18, no. 3, pp. 3873–3888, 2021

  40. [48]

    Reproducibility and Replicability of Web Mea- surement Studies,

    N. Demir, M. Große-Kampmann, T. Urban, C. Wressnegger, T. Holz, and N. Pohlmann, “Reproducibility and Replicability of Web Mea- surement Studies,” inThe ACM Web Conference (WWW), 2022, p. 533–544

  41. [49]

    Disconnect,

    Disconnect, “Disconnect,” [Online]. Available: https://disconnect.m e/trackerprotection, accessed: Jul. 25, 2025

  42. [50]

    The Menlo Report: Ethical Principles Guiding Information and Communication Technology Research,

    D. K. Dittrich and E. Kenneally, “The Menlo Report: Ethical Principles Guiding Information and Communication Technology Research,” U.S. Department of Homeland Security, Science and Technology Directorate, Cyber Security Division, Technical Report DHS–CSD–CP–12–0001, 2012

  43. [51]

    Duumviri-NDSS25,

    dlgroupuoft, “Duumviri-NDSS25,” [Online]. Available: https://gith ub.com/dlgroupuoft/Duumviri-NDSS25/tree/main, accessed: Nov. 14, 2025

  44. [52]

    Threat modeling,

    V . Drake, “Threat modeling,” [Online]. Available: https://owasp.or g/www-community/Threat Modeling, accessed: Jul. 29, 2025

  45. [53]

    Detecting Third-Party User Trackers with Cookie Files,

    V . Dudykevych and V . Nechypor, “Detecting Third-Party User Trackers with Cookie Files,” inInternational Scientific-Practical Conference Problems of Infocommunications Science and Technol- ogy (PIC S&T), 2016, pp. 78–80

  46. [54]

    EasyList,

    EasyList, “EasyList,” [Online]. Available: https://easylist.to/easylist /easylist.txt, accessed: Jul. 25, 2025

  47. [55]

    EasyPrivacy,

    EasyPrivacy, “EasyPrivacy,” [Online]. Available: https://easylist.to/ easylist/easyprivacy.txt, accessed: Jul. 25, 2025

  48. [56]

    How Unique Is Your Web Browser?

    P. Eckersley, “How Unique Is Your Web Browser?” inInternational Conference on Privacy Enhancing Technologies (PETS), 2010, p. 1–18

  49. [57]

    Client-side and Server-side Tracking on Meta: Effectiveness and Accuracy,

    A. El fraihi, N. Amieur, W. Rudametkin, and O. Goga, “Client-side and Server-side Tracking on Meta: Effectiveness and Accuracy,” Privacy Enhancing Technologies Symposium (PETS), pp. 24–44, 2024

  50. [58]

    Privacy Badger,

    Electronic Frontier Foundation, “Privacy Badger,” [Online]. Avail- able: https://privacybadger.org/, accessed: Jul. 25, 2025

  51. [59]

    Available: https://www.scopus.com/s earch/form.uri, accessed: Jul

    Elsevier, “Scopus,” [Online]. Available: https://www.scopus.com/s earch/form.uri, accessed: Jul. 27, 2025

  52. [60]

    No boundaries: Exfiltra- tion of personal data by session-replay scripts,

    S. Englehardt, G. Acar, and A. Narayanan, “No boundaries: Exfiltra- tion of personal data by session-replay scripts,” [Online]. Available: https://blog.citp.princeton.edu/2017/11/15/no-boundaries-exfiltratio n-of-personal-data-by-session-replay-scripts/, published: Nov. 15, 20...

  53. [61]

    Online Tracking: A 1-million-site Measurement and Analysis,

    S. Englehardt and A. Narayanan, “Online Tracking: A 1-million-site Measurement and Analysis,” inACM Conference on Computer and Communications Security (CCS), 2016, p. 1388–1401

  54. [62]

    Web Tracking – A Literature Review on the State of Research,

    T. Ermakova, B. Fabian, B. Bender, and K. Klimek, “Web Tracking – A Literature Review on the State of Research,” inHawaii Inter- national Conference on System Sciences (HICSS), 2018, pp. 1–10

  55. [63]

    Systematizing Systematization of Knowledge,

    D. Evans, “Systematizing Systematization of Knowledge,” [Online]. Available: https://oaklandsok.github.io/, accessed: Apr. 10, 2025

  56. [64]

    Searching two or more databases decreased the risk of missing relevant studies: a metaresearch study,

    H. Ewald, I. Klerings, G. Wagner, T. L. Heise, J. M. Stratil, S. K. Lhachimi, L. G. Hemkens, G. Gartlehner, S. Armijo-Olivo, and B. Nussbaumer-Streit, “Searching two or more databases decreased the risk of missing relevant studies: a metaresearch study,”Journal of Clinical Epi...

  57. [65]

    FPGuard: Detection and Prevention of Browser Fingerprinting,

    A. FaizKhademi, M. Zulkernine, and K. Weldemariam, “FPGuard: Detection and Prevention of Browser Fingerprinting,” inIFIP An- nual Conference on Data and Applications Security and Privacy (DBSec), 2015, pp. 293–308

  58. [66]

    Fanboy’s Annoyance List,

    Fanboy’s Annoyance List, “Fanboy’s Annoyance List,” [Online]. Available: https://easylist.to/easylist/fanboy- annoyance.txt, accessed: Aug. 13, 2025

  59. [67]

    Fanboy’s Social Blocking List,

    Fanboy’s Social Blocking List, “Fanboy’s Social Blocking List,” [Online]. Available: https://easylist.to/easylist/fanboy-social.txt, accessed: Aug. 13, 2025

  60. [68]

    Increasing User’s Privacy Control through Flexible Web Bug Detection,

    F. Fonseca, R. Pinto, and W. Meira, “Increasing User’s Privacy Control through Flexible Web Bug Detection,” inLatin American Web Congress (LA-WEB), 2005, pp. 205–212

  61. [69]

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

    I. Fouad, N. Bielova, A. Legout, and N. Sarafijanovic-Djukic, “Missed by Filter Lists: Detecting Unknown Third-Party Trackers with Invisible Pixels,”Privacy Enhancing Technologies Symposium (PETS), pp. 499 – 518, 2020

  62. [70]

    The Devil is in the Details: Detection, Measurement and Lawfulness of Server-Side Tracking on the Web,

    I. Fouad, C. Santos, and P. Laperdrix, “The Devil is in the Details: Detection, Measurement and Lawfulness of Server-Side Tracking on the Web,”Privacy Enhancing Technologies Symposium (PETS), pp. 450–465, 2024

  63. [71]

    Some common statistical methods for assessing rater agreement in radiological studies,

    M. Geijer, M. B ˚ath, and C. Wessman, “Some common statistical methods for assessing rater agreement in radiological studies,”Acta Radiologica, vol. 66, no. 6, pp. 675–683, 2025

  64. [72]

    Gerlach, C

    B. Gerlach, C. Untucht, and A. Stefan,Electronic Lab Notebooks and Experimental Design Assistants. Springer Cham, 2020, pp. 257–275

  65. [73]

    Read Between the Lines: Detect- ing Tracking JavaScript with Bytecode Classification,

    M. Ghasemisharif and J. Polakis, “Read Between the Lines: Detect- ing Tracking JavaScript with Bytecode Classification,” inACM Con- ference on Computer and Communications Security (CCS), 2023, p. 3475–3489

  66. [74]

    Ghostery,

    Ghostery, “Ghostery,” [Online]. Available: https://www.ghostery.c om, accessed: Jul. 25, 2025

  67. [75]

    WhoTracks.Me,

    ——, “WhoTracks.Me,” [Online]. Available: https://www.ghostery .com/whotracksme/trackers, accessed: Aug. 26, 2025

  68. [76]

    Network Analysis of Third Party Tracking: User Exposure to Tracking Cookies through Search,

    R. Gomer, E. M. Rodrigues, N. Milic-Frayling, and M. Schraefel, “Network Analysis of Third Party Tracking: User Exposure to Tracking Cookies through Search,” inIEEE/WIC International Joint Conferences on Web Intelligence and Intelligent Agent Technologies (WI-IAT), vol. 1, 201...

  69. [77]

    Chrome Browser,

    Google LLC, “Chrome Browser,” [Online]. Available: https://www. google.com/chrome/, accessed: Jul. 25, 2025

  70. [78]

    Google Scholar,

    ——, “Google Scholar,” [Online]. Available: https://scholar.google .com, accessed: Jul. 25, 2025

  71. [79]

    Puppeteer,

    ——, “Puppeteer,” [Online]. Available: https://pptr.dev/, accessed: Aug. 14, 2025

  72. [80]

    The Chrome User Experience Report,

    ——, “The Chrome User Experience Report,” [Online]. Available: https://developer.chrome.com/docs/crux/, accessed: Aug. 06, 2025

  73. [81]

    On Analyzing Third-party Tracking via Machine Learning,

    A. Guarino, D. Malandrino, R. Zaccagnino, F. Cozza, and A. Ra- puano, “On Analyzing Third-party Tracking via Machine Learning,” inInternational Conference on Information Systems Security and Privacy (ICISSP), 2020, pp. 532–539

  74. [82]

    An Automated Approach for Complementing Ad Blockers’ Blacklists,

    D. Gugelmann, M. Happe, B. Ager, and V . Lenders, “An Automated Approach for Complementing Ad Blockers’ Blacklists,”Privacy Enhancing Technologies Symposium (PETS), pp. 282–298, 2015

  75. [83]

    Beyond Google Scholar, Scopus, and Web of Science: An evaluation of the backward and forward citation cover- age of 59 databases’ citation indices,

    M. Gusenbauer, “Beyond Google Scholar, Scopus, and Web of Science: An evaluation of the backward and forward citation cover- age of 59 databases’ citation indices,”Research synthesis methods, vol. 15, pp. 802–817, 2024

  76. [84]

    GRADE: an emerging consensus on rating quality of evidence and strength of recommendations,

    G. H. Guyatt, A. D. Oxman, G. E. Vist, R. Kunz, Y . Falck-Ytter, P. Alonso-Coello, H. J. Sch ¨unemann, and GRADE Working Group, “GRADE: an emerging consensus on rating quality of evidence and strength of recommendations,”British Medical Jounral (BMJ), vol. 336, no. 7650, pp. 9...

  77. [85]

    Computing inter-rater reliability and its variance in the presence of high agreement,

    K. L. Gwet, “Computing inter-rater reliability and its variance in the presence of high agreement,”British Journal of Mathematical and Statistical Psychology (Br. J. Math. Stat. Psychol.), vol. 61, no. 1, pp. 29–48, 2008

  78. [86]

    Building a Scalable Web Tracking Detection System: Implementation and the Empirical Study,

    Y . Haga, Y . Takata, M. Akiyama, and T. Mori, “Building a Scalable Web Tracking Detection System: Implementation and the Empirical Study,”IEICE Transactions on Information and Systems (TRANS. INF . & SYST.), vol. E100.D, no. 8, pp. 1663–1670, 2017

  79. [87]

    SINBAD: Saliency- informed detection of breakage caused by ad blocking ,

    S. E. Hajj Chehade, S. Siby, and C. Troncoso, “ SINBAD: Saliency- informed detection of breakage caused by ad blocking ,” inIEEE Symposium on Security and Privacy (S&P), 2024, pp. 258–276

  80. [88]

    Web Execution Bundles: Reproducible, Accurate, and Archivable Web Measure- ments,

    F. Hantke, P. Snyder, H. Haddadi, and B. Stock, “Web Execution Bundles: Reproducible, Accurate, and Archivable Web Measure- ments,” inUSENIX Security Symposium (USENIX Security), 2025, pp. 8235–8253

  81. [89]

    A longitudinal analysis of online ad-blocking blacklists,

    S. S. Hashmi, M. Ikram, and M. A. Kaafar, “A longitudinal analysis of online ad-blocking blacklists,” inIEEE LCN Symposium on Emerging Topics in Networking (LCN Symposium), 2019, pp. 158– 165

  82. [90]

    Con- siderations for implementing electronic laboratory notebooks in an academic research environment,

    S. G. Higgins, A. A. Nogiwa-Valdez, and M. M. Stevens, “Con- siderations for implementing electronic laboratory notebooks in an academic research environment,”Nature Protocols (Nat. Protoc.), vol. 17, no. 2, pp. 179–189, 2022

  83. [91]

    Citation tracking for systematic literature searching: A scoping review,

    J. Hirt, T. Nordhausen, C. Appenzeller-Herzog, and H. Ewald, “Citation tracking for systematic literature searching: A scoping review,”Research Synthesis Methods, vol. 14, no. 3, pp. 563–579, 2023

  84. [92]

    CCCC: Corralling Cookies into Categories with CookieMonster,

    X. Hu, N. Sastry, and M. Mondal, “CCCC: Corralling Cookies into Categories with CookieMonster,” inACM Web Science Conference (WebSci), 2021, pp. 234–242

  85. [93]

    IBM X-Force Exchange,

    IBM Security, “IBM X-Force Exchange,” [Online]. Available: https: //exchange.xforce.ibmcloud.com/, accessed: Aug. 14, 2025

  86. [94]

    Towards Seamless Tracking-Free Web: Improved Detection of Trackers via One-class Learning,

    M. Ikram, H. J. Asghar, M. A. Kaafar, A. Mahanti, and B. Krishna- murthy, “Towards Seamless Tracking-Free Web: Improved Detection of Trackers via One-class Learning,”Privacy Enhancing Technolo- gies Symposium (PETS), pp. 79–99, 2017

  87. [95]

    IEEE Xplore Digital Library,

    Institute of Electrical and Electronics Engineers, “IEEE Xplore Digital Library,” [Online]. Available: https://ieeexplore.ieee.org/ Xplore/home.jsp, accessed: Jul. 27, 2025

  88. [96]

    Fingerprinting the Finger- printers: Learning to Detect Browser Fingerprinting Behaviors,

    U. Iqbal, S. Englehardt, and Z. Shafiq, “Fingerprinting the Finger- printers: Learning to Detect Browser Fingerprinting Behaviors,” in IEEE Symposium on Security and Privacy (S&P), 2021, pp. 1143– 1161

  89. [97]

    The Ad Wars: Retrospective Measurement and Analysis of Anti-Adblock Filter Lists,

    U. Iqbal, Z. Shafiq, and Z. Qian, “The Ad Wars: Retrospective Measurement and Analysis of Anti-Adblock Filter Lists,” inACM Internet Measurement Conference (IMC), 2017, p. 171–183

  90. [98]

    AdGraph: A Graph-Based Approach to Ad and Tracker Blocking,

    U. Iqbal, P. Snyder, S. Zhu, B. Livshits, Z. Qian, and Z. Shafiq, “AdGraph: A Graph-Based Approach to Ad and Tracker Blocking,” inIEEE Symposium on Security and Privacy (S&P), 2020, pp. 763– 776

  91. [99]

    Khaleesi: Breaker of Advertising and Tracking Request Chains,

    U. Iqbal, C. Wolfe, C. Nguyen, S. Englehardt, and Z. Shafiq, “Khaleesi: Breaker of Advertising and Tracking Request Chains,” in USENIX Security Symposium (USENIX Security), 2022, pp. 2911– 2928

  92. [100]

    IEEE SP 2022 WtaGraph,

    jun521ju, “IEEE SP 2022 WtaGraph,” [Online]. Available: https: //github.com/jun521ju/IEEE SP 2022 WtaGraph, accessed: Nov. 14, 2025

  93. [101]

    Towards Automatic Identification of JavaScript-oriented Machine-Based Tracking,

    A. J. Kaizer and M. Gupta, “Towards Automatic Identification of JavaScript-oriented Machine-Based Tracking,” inACM on Interna- tional Workshop on Security And Privacy Analytics (IWSPA), 2016, p. 33–40

  94. [102]

    Like a Pack of Wolves: Community Structure of Web Trackers,

    V . Kalavri, J. Blackburn, M. Varvello, and K. Papagiannaki, “Like a Pack of Wolves: Community Structure of Web Trackers,” inIn- ternational Conference on Passive and Active Measurement (PAM), 2016, pp. 42–54

  95. [103]

    Carnus: Exploring the Privacy Threats of Browser Extension Fingerprinting,

    S. Karami, P. Ilia, K. Solomos, and J. Polakis, “Carnus: Exploring the Privacy Threats of Browser Extension Fingerprinting,” inNet- work and Distributed System Security Symposium (NDSS), 2020

  96. [104]

    Wide-AdGraph: Detecting Ad Trackers with a Wide Dependency Chain Graph,

    A. H. Kargaran, M. S. Akhondzadeh, M. R. Heidarpour, M. H. Manshaei, K. Salamatian, and M. Nejad Sattary, “Wide-AdGraph: Detecting Ad Trackers with a Wide Dependency Chain Graph,” in ACM Web Science Conference (WebSci), 2021, p. 253–261

  97. [105]

    Guidelines for performing System- atic Literature Reviews in Software Engineering,

    B. Kitchenham and S. Charters, “Guidelines for performing System- atic Literature Reviews in Software Engineering,” Keele University and Durham University, Tech. Rep., 2007

  98. [106]

    In-Depth Evaluation of Redirect Tracking and Link Usage,

    M. Koop, E. Tews, and S. Katzenbeisser, “In-Depth Evaluation of Redirect Tracking and Link Usage,”Privacy Enhancing Technolo- gies Symposium (PETS), pp. 394–413, 2020

  99. [107]

    Krippendorff,Content Analysis: An Introduction to Its Method- ology, 4th ed

    K. Krippendorff,Content Analysis: An Introduction to Its Method- ology, 4th ed. SAGE Publications, Inc., 2019

  100. [108]

    Generating a Privacy Footprint on the Internet,

    B. Krishnamurthy and C. E. Wills, “Generating a Privacy Footprint on the Internet,” inACM Conference on Internet Measurement (IMC), 2006, p. 65–70

  101. [109]

    On the Leakage of Personally Identifiable Information Via Online Social Networks,

    ——, “On the Leakage of Personally Identifiable Information Via Online Social Networks,” inACM Workshop on Online Social Networks (WOSN), 2009, p. 7–12

  102. [110]

    HTTP Cookies: Standards, Privacy, and Politics,

    D. M. Kristol, “HTTP Cookies: Standards, Privacy, and Politics,” ACM Transactions Internet Technolology (TOIT), vol. 1, no. 2, p. 151–198, 2001

  103. [111]

    An Update for Taxonomy Designers,

    D. Kundisch, J. Muntermann, A. M. Oberl ¨ander, D. Rau, M. R ¨oglinger, T. Schoormann, and D. Szopinski, “An Update for Taxonomy Designers,”Business & Information Systems Engineering (BISE), vol. 64, no. 4, pp. 421–439, 2022

  104. [112]

    Learning to remove Internet advertisements,

    N. Kushmerick, “Learning to remove Internet advertisements,” in International Conference on Autonomous Agents (AGENTS), 1999, pp. 175–181

  105. [113]

    Browser Fingerprinting: A Survey,

    P. Laperdrix, N. Bielova, B. Baudry, and G. Avoine, “Browser Fingerprinting: A Survey,”ACM Transactions on the Web (TWEB), vol. 14, no. 2, 2020

  106. [114]

    Beauty and the Beast: Diverting Modern Web Browsers to Build Unique Browser Fingerprints,

    P. Laperdrix, W. Rudametkin, and B. Baudry, “Beauty and the Beast: Diverting Modern Web Browsers to Build Unique Browser Fingerprints,” inIEEE Symposium on Security and Privacy (S&P), 2016, pp. 878–894

  107. [115]

    Fingerprinting in Style: Detecting Browser Extensions via Injected Style Sheets,

    P. Laperdrix, O. Starov, Q. Chen, A. Kapravelos, and N. Nikiforakis, “Fingerprinting in Style: Detecting Browser Extensions via Injected Style Sheets,” inUSENIX Security Symposium (USENIX Security), 2021, pp. 2507–2524

  108. [116]

    AdCPG: Classifying JavaScript Code Prop- erty Graphs with Explanations for Ad and Tracker Blocking,

    C. Lee and S. Son, “AdCPG: Classifying JavaScript Code Prop- erty Graphs with Explanations for Ad and Tracker Blocking,” in ACM Conference on Computer and Communications Security (CCS), 2023, pp. 3505–3518

  109. [117]

    Net-track: Generic Web Tracking Detection Using Packet Metadata,

    D. Lee, M. Joo, and W. Lee, “Net-track: Generic Web Tracking Detection Using Packet Metadata,” inThe ACM Web Conference (WWW), 2023, pp. 2230–2240

  110. [118]

    AdFlush: A Real- World Deployable Machine Learning Solution for Effective Adver- tisement and Web Tracker Prevention,

    K. Lee, C. Lim, B. Jin, T. Kim, and H. Kim, “AdFlush: A Real- World Deployable Machine Learning Solution for Effective Adver- tisement and Web Tracker Prevention,” inThe ACM Web Conference (WWW), 2024, pp. 1902–1913

  111. [119]

    Internet Jones and the Raiders of the Lost Trackers: An Archaeological Study of Web Tracking from 1996 to 2016,

    A. Lerner, A. K. Simpson, T. Kohno, and F. Roesner, “Internet Jones and the Raiders of the Lost Trackers: An Archaeological Study of Web Tracking from 1996 to 2016,” inUSENIX Security Symposium (USENIX Security), 2016, p. 997–1013

  112. [120]

    Shujun Li’s Online Bibliography of SoK (Systematization of Knowledge) Papers,

    S. Li, “Shujun Li’s Online Bibliography of SoK (Systematization of Knowledge) Papers,” [Online]. Available: https://www.hooklee.co m/Research/SoK/SoK.html, accessed: Apr. 10, 2025

  113. [121]

    TrackAd- visor: Taking Back Browsing Privacy from Third-Party Trackers

    T.-C. Li, H. Hang, M. Faloutsos, and P. Efstathopoulos, “TrackAd- visor: Taking Back Browsing Privacy from Third-Party Trackers”,” inInternational Conference on Passive and Active Measurement (PAM), 2015, pp. 277–289

  114. [122]

    FPFlow: Detect and Prevent Browser Fingerprinting with Dynamic Taint Analysis,

    T. Li, X. Zheng, K. Shen, and X. Han, “FPFlow: Detect and Prevent Browser Fingerprinting with Dynamic Taint Analysis,” inChina Cyber Security Conference (CNCERT), W. Lu, Y . Zhang, W. Wen, H. Yan, and C. Li, Eds., 2022, pp. 51–67

  115. [123]

    Exposing the Invisible Web: An Analysis of Third-Party HTTP Requests on 1 Million Websites,

    T. Libert, “Exposing the Invisible Web: An Analysis of Third-Party HTTP Requests on 1 Million Websites,”International Journal of Communication (IJoC), vol. 9, no. 0, 2015

  116. [124]

    AdFlush: Source code and environment for AdFlush,

    C. Lim and K. Lee, “AdFlush: Source code and environment for AdFlush,” [Online]. Available: https://doi.org/10.5281/zenodo.106 82483, accessed: Nov. 14, 2025

  117. [125]

    Peter Lowe’s Blocklist,

    P. Lowe, “Peter Lowe’s Blocklist,” [Online]. Available: https://pgl. yoyo.org/as/serverlist.php?showintro=0;hostformat=hosts, accessed: Aug. 13, 2025

  118. [126]

    Ad Service Detection - A Comparative Study Using Machine Learning Techniques,

    Y . K. M, S. S, and T. M. G, “Ad Service Detection - A Comparative Study Using Machine Learning Techniques,” inInternational Con- ference on Computing Communication and Networking Technologies (ICCCNT), 2022, pp. 1–7

  119. [127]

    The Majestic Million,

    Majestic, “The Majestic Million,” [Online]. Available: https://maje stic.com/reports/majestic-million, accessed: Jul. 25, 2025

  120. [128]

    hphosts,

    Malwarebytes, “hphosts,” [Online]. Available: https://web.archive. org/web/20160326122251/http://hosts-file.net/, accessed: Aug. 14, 2025

  121. [129]

    Hidden Surveillance by Web Sites: Web Bugs in Contemporary Use,

    D. Martin, H. Wu, and A. Alsaid, “Hidden Surveillance by Web Sites: Web Bugs in Contemporary Use,”Communnications of the ACM (CACM), vol. 46, no. 12, p. 258–264, 2003

  122. [130]

    Web Tracking Domain and Possible Privacy Defending Tools: A Literature Review,

    M. F. Maryam Abdulaziz Saad Bubukayr, “Web Tracking Domain and Possible Privacy Defending Tools: A Literature Review,”Jour- nal of Cyber Security, vol. 4, no. 2, pp. 79–94, 2022

  123. [131]

    Third-Party Web Tracking: Policy and Technology,

    J. R. Mayer and J. C. Mitchell, “Third-Party Web Tracking: Policy and Technology,” inIEEE Symposium on Security and Privacy (S&P), 2012, pp. 413–427

  124. [132]

    A comparison of web pri- vacy protection techniques,

    J. Mazel, R. Garnier, and K. Fukuda, “A comparison of web pri- vacy protection techniques,”Computer Communications (Comput. Commun.), vol. 144, no. C, p. 162–174, 2019

  125. [133]

    No Escape From Reality: Security and Privacy of Augmented Reality Browsers,

    R. McPherson, S. Jana, and V . Shmatikov, “No Escape From Reality: Security and Privacy of Augmented Reality Browsers,” inThe ACM Web Conference (WWW), 2015, p. 743–753

  126. [134]

    (Do Not) Track Me Sometimes: Users’ Contextual Preferences for Web Tracking,

    W. Melicher, M. Sharif, J. Tan, L. Bauer, M. Christodorescu, and P. Leon, “(Do Not) Track Me Sometimes: Users’ Contextual Preferences for Web Tracking,”Privacy Enhancing Technologies Symposium (PETS), pp. 135–154, 2016

  127. [135]

    You Can’t Trust Your Tag Neither: Privacy Leaks and Potential Legal Violations within the Google Tag Manager ,

    G. Mertens, N. Bielova, V . Roca, and C. Santos, “ You Can’t Trust Your Tag Neither: Privacy Leaks and Potential Legal Violations within the Google Tag Manager ,” inEuropean Symposium on Security and Privacy (EuroS&P). IEEE Computer Society, 2025, pp. 93–112

  128. [136]

    Block Me If You Can: A Large- Scale Study of Tracker-Blocking Tools,

    G. Merzdovnik, M. Huber, D. Buhov, N. Nikiforakis, S. Neuner, M. Schmiedecker, and E. Weippl, “Block Me If You Can: A Large- Scale Study of Tracker-Blocking Tools,” inIEEE European Sympo- sium on Security and Privacy (EuroS&P), 2017, pp. 319–333

  129. [137]

    Unsupervised Detection of Web Trackers,

    H. Metwalley, S. Traverso, and M. Mellia, “Unsupervised Detection of Web Trackers,” inIEEE Global Communications Conference (GLOBECOM), 2015, pp. 1–6

  130. [138]

    Don’t Count Me Out: On the Relevance of IP Address in the Tracking Ecosystem,

    V . Mishra, P. Laperdrix, A. Vastel, W. Rudametkin, R. Rouvoy, and M. Lopatka, “Don’t Count Me Out: On the Relevance of IP Address in the Tracking Ecosystem,” inThe ACM Web Conference (WWW), 2020, p. 808–815

  131. [139]

    Targeted and Troublesome: Tracking and Advertising on Children’s Websites,

    Z. Moti, A. Senol, H. Bostani, F. Z. Borgesius, V . Moonsamy, A. Mathur, and G. Acar, “Targeted and Troublesome: Tracking and Advertising on Children’s Websites,” inIEEE Symposium on Security and Privacy (S&P), 2024, pp. 1517–1535

  132. [140]

    Pixel Perfect: Fingerprinting Can- vas in HTML5,

    K. Mowery and H. Shacham, “Pixel Perfect: Fingerprinting Can- vas in HTML5,” inWorkshop on Web 2.0 Security and Privacy (WW2SP), 2012

  133. [141]

    Firefox Browser,

    Mozilla Corporation, “Firefox Browser,” [Online]. Available: https: //www.mozilla.org/en-US/firefox/new/, accessed: Jul. 25, 2025

  134. [142]

    Shadowed realities: an investigation of UI attacks in We- bXR,

    C. Mukherjee, R. Mohamed, A. Arunasalam, H. Farrukh, and Z. B. Celik, “Shadowed realities: an investigation of UI attacks in We- bXR,” inUSENIX Security Symposium (USENIX Security), 2025, pp. 1549–1568

  135. [143]

    Rationale for systematic reviews,

    C. D. Mulrow, “Rationale for systematic reviews,”British Medical Jounral (BMJ), vol. 309, no. 6954, pp. 597–599, 1994

  136. [144]

    Munir, “PURL,” [Online]

    S. Munir, “PURL,” [Online]. Available: https://github.com/shaoorm unir/purl, accessed: Nov. 14, 2025

  137. [145]

    PURL 20,000 Websites Dataset,

    ——, “PURL 20,000 Websites Dataset,” [Online]. Available: https: //zenodo.org/records/12667973, accessed: Nov. 14, 2025

  138. [146]

    PURL: Safe and Effective Sanitization of Link Decoration,

    S. Munir, P. Lee, U. Iqbal, S. Siby, and Z. Shafiq, “PURL: Safe and Effective Sanitization of Link Decoration,” inUSENIX Security Symposium (USENIX Security), 2024

  139. [147]

    CookieGraph: Understanding and Detecting First- Party Tracking Cookies,

    S. Munir, S. Siby, U. Iqbal, S. Englehardt, Z. Shafiq, and C. Troncoso, “CookieGraph: Understanding and Detecting First- Party Tracking Cookies,” inACM Conference on Computer and Communications Security (CCS), 2023, p. 3490–3504

  140. [148]

    Beyond the Crawl: Unmasking Browser Fingerprinting in Real User Interactions,

    M. S. Muthu Selva Annamalai, E. De Cristofaro, and I. Bilogrevic, “Beyond the Crawl: Unmasking Browser Fingerprinting in Real User Interactions,” inThe ACM Web Conference (WWW), 2025, p. 3896–3907

  141. [149]

    A method for taxonomy development and its application in information systems,

    R. C. Nickerson, U. Varshney, and J. Muntermann, “A method for taxonomy development and its application in information systems,” European Journal of Information Systems (EJIS), vol. 22, no. 3, pp. 336–359, 2013

  142. [150]

    Cookieless Monster: Exploring the Ecosystem of Web-Based Device Fingerprinting,

    N. Nikiforakis, A. Kapravelos, W. Joosen, C. Kruegel, F. Piessens, and G. Vigna, “Cookieless Monster: Exploring the Ecosystem of Web-Based Device Fingerprinting,” inIEEE Symposium on Security and Privacy (S&P), 2013, pp. 541–555

  143. [151]

    Get in Researchers; We’re Measuring Reproducibility

    D. Olszewski, A. Lu, C. Stillman, K. Warren, C. Kitroser, A. Pas- cual, D. Ukirde, K. Butler, and P. Traynor, “Get in Researchers; We’re Measuring Reproducibility”: A Reproducibility Study of Ma- chine Learning Papers in Tier 1 Security Conferences,” inACM Con- ference on Comp...

  144. [152]

    An Approach for Identifying JavaScript-loaded Advertisements through Static Program Analysis,

    C. R. Orr, A. Chauhan, M. Gupta, C. J. Frisz, and C. W. Dunn, “An Approach for Identifying JavaScript-loaded Advertisements through Static Program Analysis,” inACM Workshop on Privacy in the Electronic Society (WPES), 2012, p. 1–12

  145. [153]

    Gray literature: An important resource in systematic reviews,

    A. Paez, “Gray literature: An important resource in systematic reviews,”Journal of Evidince-Based Medicine (EMB), vol. 10, no. 3, pp. 233–240, 2017

  146. [154]

    PRISMA 2020 explanation and elaboration: updated guidance and exemplars for reporting system- atic reviews,

    M. J. Page, D. Moher, P. M. Bossuyt, I. Boutron, T. C. Hoffmann, C. D. Mulrow, L. Shamseer, J. M. Tetzlaff, E. A. Akl, S. E. Brennan, R. Chou, J. Glanville, J. M. Grimshaw, A. Hr ´objartsson, M. M. Lalu, T. Li, E. W. Loder, E. Mayo-Wilson, S. McDonald, L. A. McGuinness, L. A. ...

  147. [155]

    User Tracking in the Post-cookie Era: How Websites Bypass GDPR Consent to Track Users,

    E. Papadogiannakis, P. Papadopoulos, N. Kourtellis, and E. P. Markatos, “User Tracking in the Post-cookie Era: How Websites Bypass GDPR Consent to Track Users,” inThe ACM Web Confer- ence (WWW), 2021, p. 2130–2141

  148. [156]

    Cookie Synchro- nization: Everything You Always Wanted to Know But Were Afraid to Ask,

    P. Papadopoulos, N. Kourtellis, and E. Markatos, “Cookie Synchro- nization: Everything You Always Wanted to Know But Were Afraid to Ask,” inThe ACM Web Conference (WWW), 2019, p. 1432–1442

  149. [157]

    Tranco: A Research-Oriented Top Sites Ranking Hardened Against Manipulation,

    V . L. Pochat, T. van Goethem, S. Tajalizadehkhoob, M. Korczy ´nski, and W. Joosen, “Tranco: A Research-Oriented Top Sites Ranking Hardened Against Manipulation,”Network and Distributed System Security Symposium (NDSS), 2018

  150. [158]

    JShelter: Give Me My Browser Back:,

    L. Pol ˇc´ak, M. Salo ˇn, G. Maone, R. Hranick ´y, and M. McMahon, “JShelter: Give Me My Browser Back:,” inInternational Conference on Security and Cryptography (SECRYPT), 2023, pp. 287–294

  151. [159]

    Artifact- evaluation.md,

    Privacy Enhancing Technologies Symposium, “Artifact- evaluation.md,” [Online]. Available: https://petsymposium.o rg/files/ARTIFACT-APPENDIX.md, accessed: Jul. 27, 2025

  152. [160]

    Available: https://petsymposium.org/pop ets/, accessed: Jul

    ——, “PoPETs,” [Online]. Available: https://petsymposium.org/pop ets/, accessed: Jul. 27, 2025

  153. [161]

    DeepFPD: Browser Fingerprinting Detection via Deep Learning With Multimodal Learn- ing and Attention,

    W. Qiang, K. Ren, Y . Wu, D. Zou, and H. Jin, “DeepFPD: Browser Fingerprinting Detection via Deep Learning With Multimodal Learn- ing and Attention,”IEEE Transactions on Reliability, vol. 73, no. 3, pp. 1516–1528, 2024

  154. [162]

    Quantcast,

    Quantcast, “Quantcast,” [Online]. Available: https://www.quantcast. com/advertiser, accessed: Aug. 06, 2025

  155. [163]

    Measuring UID Smuggling in the Wild,

    A. Randall, P. Snyder, A. Ukani, A. C. Snoeren, G. M. V oelker, S. Savage, and A. Schulman, “Measuring UID Smuggling in the Wild,” inACM Internet Measurement Conference (IMC), 2022, p. 230–243

  156. [164]

    Tranco 16-5-22 top 10K crawled with T.EX,

    P. Raschke, “Tranco 16-5-22 top 10K crawled with T.EX,” [Online]. Available: https://doi.org/10.5281/zenodo.7123945, 2023, accessed: Nov. 14, 2025

  157. [165]

    Tranco 29-3-23 top 10K crawled with T.EX,

    ——, “Tranco 29-3-23 top 10K crawled with T.EX,” [Online]. Available: https://doi.org/10.5281/zenodo.11555919, 2023, accessed: Nov. 14, 2025

  158. [166]

    t.ex-Graph: Automated Web Tracker Detection Using Centrality Metrics and Data Flow Characteristics:,

    P. Raschke, P. Herbke, and H. Schwerdtner, “t.ex-Graph: Automated Web Tracker Detection Using Centrality Metrics and Data Flow Characteristics:,” inInternational Conference on Information Sys- tems Security and Privacy (ICISSP), 2023, pp. 199–209

  159. [167]

    Towards Real-Time Web Tracking Detection with T.EX - The Transparency EXtension,

    P. Raschke, S. Zickau, J. L. Kr ¨oger, and A. K ¨upper, “Towards Real-Time Web Tracking Detection with T.EX - The Transparency EXtension,” inAnnual Privacy Forum (APF), 2019, pp. 3–17

  160. [168]

    Anti-Adblock Killer,

    Reek, “Anti-Adblock Killer,” [Online]. Available: https://github.com /reek/anti-adblock-killer/blob/master/anti-adblock-killer-filters.txt, accessed: Aug. 13, 2025

  161. [169]

    ML-CB: Machine Learning Can- vas Block,

    N. Reitinger and M. L. Mazurek, “ML-CB: Machine Learning Can- vas Block,”Privacy Enhancing Technologies Symposium (PETS), pp. 453–473, 2021

  162. [170]

    Beyond the Request: Har- nessing HTTP Response Headers for Cross-Browser Web Tracker Classification in an Imbalanced Setting,

    W. Rieder, P. Raschke, and T. Cory, “Beyond the Request: Har- nessing HTTP Response Headers for Cross-Browser Web Tracker Classification in an Imbalanced Setting,”Privacy Enhancing Tech- nologies Symposium (PETS), pp. 100–117, 2025

  163. [171]

    SoK: After Decades of Web Tracker Detection, What’s Next?: Supplementary Artifact,

    W. Rieder, P. Raschke, T. Cory, C. R. Sechting, A. Kumar, and K. Axel, “SoK: After Decades of Web Tracker Detection, What’s Next?: Supplementary Artifact,” [Online]. Available: https://doi.org/ 10.5281/zenodo.19240311, 2026, accessed: Mar. 26, 2026

  164. [172]

    Unveiling Web Fingerprinting in the Wild Via Code Mining and Machine Learning,

    V . Rizzo, S. Traverso, and M. Mellia, “Unveiling Web Fingerprinting in the Wild Via Code Mining and Machine Learning,”Privacy Enhancing Technologies Symposium (PETS), pp. 43–63, 2021

  165. [173]

    Detecting and Defending Against Third-Party Tracking on the Web,

    F. Roesner, T. Kohno, and D. Wetherall, “Detecting and Defending Against Third-Party Tracking on the Web,” inUSENIX Symposium on Networked Systems Design and Implementation (NSDI), 2012, pp. 155–168

  166. [174]

    Analyzing the Effectiveness of Privacy Related Add-Ons Employed to Thwart Web Based Track- ing,

    M. Ruffell, J. B. Hong, and D. S. Kim, “Analyzing the Effectiveness of Privacy Related Add-Ons Employed to Thwart Web Based Track- ing,” inIEEE Pacific Rim International Symposium on Dependable Computing (PRDC), 2015, pp. 264–272

  167. [175]

    Toppling Top Lists: Evaluating the Accuracy of Popular Website Lists,

    K. Ruth, D. Kumar, B. Wang, L. Valenta, and Z. Durumeric, “Toppling Top Lists: Evaluating the Accuracy of Popular Website Lists,” inACM Internet Measurement Conference (IMC), 2022, p. 374–387

  168. [176]

    Knockin’ on Trackers’ Door: Large- Scale Automatic Analysis of Web Tracking,

    I. Sanchez-Rola and I. Santos, “Knockin’ on Trackers’ Door: Large- Scale Automatic Analysis of Web Tracking,” inInternational Con- ference on Detection of Intrusions and Malware, and Vulnerability Assessment (DIMVA), vol. 10885, 2018, pp. 281–302

  169. [177]

    The web is watching you: A comprehensive review of web- tracking techniques and countermeasures,

    I. Sanchez-Rola, X. Ugarte-Pedrero, I. Santos, and P. G. Bringas, “The web is watching you: A comprehensive review of web- tracking techniques and countermeasures,”Logic Journal of the IGPL, vol. 25, no. 1, pp. 18–29, 2016

  170. [178]

    Watching Them Watching Me: Browser extensions’ Impact on User Privacy Awareness and Concern,

    F. Schaub, A. Marella, P. Kalvani, B. Ur, C. Pan, E. Forney, and L. F. Cranor, “Watching Them Watching Me: Browser extensions’ Impact on User Privacy Awareness and Concern,” inNDSS Workshop on Usable Security (USEC), 2016

  171. [179]

    Chapter 14: Completing ‘Summary of findings’ tables and grading the certainty of the evidence,

    H. J. Sch ¨unemann, J. P. T. Higgins, G. E. Vist, P. Glasziou, E. A. Akl, N. Skoetz, and G. H. Guyatt, “Chapter 14: Completing ‘Summary of findings’ tables and grading the certainty of the evidence,” in Cochrane Handbook for Systematic Reviews of Interventions, J. P. T. Higgin...

  172. [180]

    Selenium,

    Selenium, “Selenium,” [Online]. Available: https://www.selenium.d ev, accessed: Jul. 25, 2025

  173. [181]

    A Mathematical Theory of Communication,

    C. E. Shannon, “A Mathematical Theory of Communication,”The Bell System Technical Journal (BSTJ), vol. 27, no. 3, pp. 379–423, 1948

  174. [182]

    Mind the Web: The Security of Web Use Agents,

    A. Shapira, P. A. Gandhi, E. Habler, and A. Shabtai, “Mind the Web: The Security of Web Use Agents,”arXiv preprint arXiv:2506.07153, 2025

  175. [183]

    Duumviri: Detecting Trackers and Mixed Trackers with a Breakage Detector,

    H. Shuang, L. Zhao, and D. Lie, “Duumviri: Detecting Trackers and Mixed Trackers with a Breakage Detector,” inNetwork and Distributed System Security Symposium (NDSS), 2025

  176. [184]

    NoMoAds: Effective and Efficient Cross-App Mobile Ad-Blocking,

    A. Shuba, A. Markopoulou, and Z. Shafiq, “NoMoAds: Effective and Efficient Cross-App Mobile Ad-Blocking,”Privacy Enhancing Technologies Symposium (PETS), pp. 125–140, 2018

  177. [185]

    WebGraph: Capturing Advertising and Tracking Information Flows for Robust Blocking,

    S. Siby, U. Iqbal, S. Englehardt, Z. Shafiq, and C. Troncoso, “WebGraph: Capturing Advertising and Tracking Information Flows for Robust Blocking,” inUSENIX Security Symposium (USENIX Security), 2022, pp. 2875–2892

  178. [186]

    Combating Web Tracking: Analyzing Web Tracking Technologies for User Privacy,

    K. Sim, H. Heo, and H. Cho, “Combating Web Tracking: Analyzing Web Tracking Technologies for User Privacy,”Future Internet, vol. 16, no. 10, 2024

  179. [187]

    EssentialFP: Exposing the Essence of Browser Fingerprinting,

    A. Sjosten, D. Hedin, and A. Sabelfeld, “EssentialFP: Exposing the Essence of Browser Fingerprinting,” inEuropean Symposium on Security and Privacy Workshops (EuroS&PW), 2021, pp. 32–48

  180. [188]

    Blocked or Broken? Automatically Detecting When Privacy Interventions Break Websites,

    M. Smith, P. Snyder, M. Haller, B. Livshits, D. Stefan, and H. Had- dadi, “Blocked or Broken? Automatically Detecting When Privacy Interventions Break Websites,”Privacy Enhancing Technologies Symposium (PETS), pp. 6–23, 2022

  181. [189]

    Who Filters the Filters: Under- standing the Growth, Usefulness and Efficiency of Crowdsourced Ad Blocking,

    P. Snyder, A. Vastel, and B. Livshits, “Who Filters the Filters: Under- standing the Growth, Usefulness and Efficiency of Crowdsourced Ad Blocking,”ACM Measurement and Analysis of Computing Systems (POMACS), vol. 4, no. 2, 2020

  182. [190]

    Tales of Favicons and Caches: Persistent Tracking in Modern Browsers,

    K. Solomos, J. Kristoff, C. Kanich, and J. Polakis, “Tales of Favicons and Caches: Persistent Tracking in Modern Browsers,” inNetwork and Distributed System Security Symposium (NDSS), 2021

  183. [191]

    Flash Cookies and Privacy,

    A. Soltani, S. Canty, Q. Mayo, L. Thomas, and C. Hoofnagle, “Flash Cookies and Privacy,”SSRN Electronic Journal, pp. 158–163, 2009

  184. [192]

    Side-channel Inference of User Activities in AR/VR Using GPU Profiling,

    S. Son, C. Mukherjee, R. M. Aburas, B. Gulmezoglu, and Z. B. Celik, “Side-channel Inference of User Activities in AR/VR Using GPU Profiling,” inNetwork and Distributed System Security Sym- posium (NDSS), 2026

  185. [193]

    SpringerLink,

    Springer Nature, “SpringerLink,” [Online], 2025, available: https: //link.springer.com/, accessed: Jul. 27, 2025

  186. [194]

    SoK: State of the Krawlers – Eval- uating the Effectiveness of Crawling Algorithms for Web Security Measurements,

    A. Stafeev and G. Pellegrino, “SoK: State of the Krawlers – Eval- uating the Effectiveness of Crawling Algorithms for Web Security Measurements,” inUSENIX Security Symposium (USENIX Security), 2024, pp. 719–737

  187. [195]

    Browser market share worldwide (jul. 2024 - jul. 2025),

    Statcounter Global Stats, “Browser market share worldwide (jul. 2024 - jul. 2025),” [Online]. Available: https://gs.statcounter.c om/browser-market-share, accessed: Aug. 06, 2025

  188. [196]

    Automatic Discovery of Emerging Browser Fingerprinting Techniques,

    J. Su and A. Kapravelos, “Automatic Discovery of Emerging Browser Fingerprinting Techniques,” inThe ACM Web Conference (WWW), 2023, pp. 2178–2188

  189. [197]

    A system for detecting third-party tracking through the combination of dynamic analysis and static analysis,

    J. Sun, Z. Huang, T. Yang, W. Wang, and Y . Zhang, “A system for detecting third-party tracking through the combination of dynamic analysis and static analysis,” inIEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), 2021, pp. 1– 6

  190. [198]

    t.ex-graph-2.0-classifier,

    t-ex-tools, “t.ex-graph-2.0-classifier,” [Online]. Available: https://gi thub.com/t-ex-tools/t.ex-graph-2.0-classifier, accessed: Nov. 14, 2025

  191. [199]

    Risk Analysis of Cookie Sharing by Link Decoration and CNAME Cloaking,

    Y . Takata, D. Ito, H. Kumagai, and M. Kamizono, “Risk Analysis of Cookie Sharing by Link Decoration and CNAME Cloaking,”Journal of Information Processing (JIP), vol. 29, pp. 649–656, 2021

  192. [200]

    Benchmark and Comparison of Tracker-blockers: Should You Trust Them?

    S. Traverso, M. Trevisan, L. Giannantoni, M. Mellia, and H. Metwal- ley, “Benchmark and Comparison of Tracker-blockers: Should You Trust Them?” in2017 Network Traffic Measurement and Analysis Conference (TMA), 2017, pp. 1–9

  193. [201]

    Available: https://ublock.org, accessed: Jul

    uBlock, “ublock,” [Online]. Available: https://ublock.org, accessed: Jul. 25, 2025

  194. [202]

    ublock filters,

    uBlock filters, “ublock filters,” [Online]. Available: https://easylist -downloads.adblockplus.org/antiadblockfilters.txt, accessed: Aug. 13, 2025

  195. [203]

    uBlock filters – Privacy,

    uBlock filters – Privacy, “uBlock filters – Privacy,” [Online]. Avail- able: https://raw.githubusercontent.com/uBlockOrigin/uAssets/mas ter/filters/privacy.txt, accessed: Aug. 13, 2025

  196. [204]

    uBlock Origin,

    uBlock Origin, “uBlock Origin,” [Online]. Available: https://github .com/gorhill/uBlock, accessed: Jul. 25, 2025

  197. [205]

    Privacy Practices of Browser Agents,

    A. Ukani, H. Haddadi, A. S. Shamsabadi, and P. Snyder, “Privacy Practices of Browser Agents,”arXiv preprint arXiv:2512.07725, 2025

  198. [206]

    USENIX Proceedings,

    USENIX Association, “USENIX Proceedings,” [Online]. Available: https://www.usenix.org/publications/proceedings, accessed: Jul. 27, 2025

  199. [207]

    USENIX Security ’25 Artifact Appendix,

    ——, “USENIX Security ’25 Artifact Appendix,” [Online]. Avail- able: https://secartifacts.github.io/usenixsec2025/appendix/usenix-s ecurity-25-appendix.pdf, accessed: Jul. 27, 2025

  200. [208]

    SoK: Advances and Open Problems in Web Tracking,

    Y . Vekaria, Y . Beugin, S. Munir, G. Acar, N. Bielova, S. Englehardt, U. Iqbal, A. Kapravelos, P. Laperdrix, N. Nikiforakis, J. Polakis, F. Roesner, Z. Shafiq, and S. Zimmeck, “SoK: Advances and Open Problems in Web Tracking,”arXiv preprint arXiv:2506.14057, 2025

  201. [209]

    ADREMOVER: THE IMPROVED MA- CHINE LEARNING APPROACH FOR BLOCKING ADS,

    T. V o and C. Jaiswal, “ADREMOVER: THE IMPROVED MA- CHINE LEARNING APPROACH FOR BLOCKING ADS,” in IEEE Annual Ubiquitous Computing, Electronics & Mobile Com- munication Conference (UEMCON), 2019, pp. 1–4

  202. [210]

    SDM- GAT: StylisticFP Detection Method Based on Graph Attention Net- work,

    X. Wang, W. Liu, C. Zheng, X. Liu, Y . Cao, and Q. Liu, “SDM- GAT: StylisticFP Detection Method Based on Graph Attention Net- work,” inInternational Conference on Advanced Data Mining and Applications (ADMA), 2025, pp. 234–249

  203. [211]

    Warning Removal List,

    Warning Removal List, “Warning Removal List,” [Online]. Avail- able: https://easylist-downloads.adblockplus.org/antiadblockfilters.t xt, accessed: Aug. 13, 2025

  204. [212]

    Tracking prevention in webkit,

    Webkit, “Tracking prevention in webkit,” [Online]. Available: https: //webkit.org/tracking-prevention/#intelligent-tracking-prevention-i tp, accessed: Jul. 30, 2025

  205. [213]

    Tracking Without Borders: Studying the Role of WebViews in Bridging Mobile and Web Tracking,

    N. Weerasekara, J. M. Moreno, S. Matic, J. Reardon, J. Tapiador, and N. Vallina-Rodr´ıguez, “Tracking Without Borders: Studying the Role of WebViews in Bridging Mobile and Web Tracking,”Privacy Enhancing Technologies Symposium (PETS), p. 745–762, 2025

  206. [214]

    SoK (or SoLK?): On the Quantitative Study of Sociodemo- graphic Factors and Computer Security Behaviors,

    M. Wei, J. Mink, Y . Eiger, T. Kohno, E. M. Redmiles, and F. Roes- ner, “SoK (or SoLK?): On the Quantitative Study of Sociodemo- graphic Factors and Computer Security Behaviors,” inUSENIX Security Symposium (USENIX Security), 2024

  207. [215]

    Oh, the Places You’ve Been! User Reactions to Longitudinal Transparency About Third-Party Web Tracking and Inferencing,

    B. Weinshel, M. Wei, M. Mondal, E. Choi, S. Shan, C. Dolin, M. L. Mazurek, and B. Ur, “Oh, the Places You’ve Been! User Reactions to Longitudinal Transparency About Third-Party Web Tracking and Inferencing,” inACM Conference on Computer and Communications Security (CCS), 2019,...

  208. [216]

    What Ad Blockers Are (and Are Not) Doing,

    C. E. Wills and D. C. Uzunoglu, “What Ad Blockers Are (and Are Not) Doing,” inIEEE Workshop on Hot Topics in Web Systems and Technologies (HotWeb), 2016, pp. 72–77

  209. [217]

    Detecting Web Tracking at the Net- work Layer,

    M. Wittig and D. Kesdo ˘gan, “Detecting Web Tracking at the Net- work Layer,” inIFIP International Conference on ICT Systems Security and Privacy Protection (SEC), vol. 679, 2024, pp. 131– 148

  210. [218]

    http-response-classifier,

    wolfrieder, “http-response-classifier,” [Online]. Available: https://gi thub.com/wolfrieder/http-response-classifier, accessed: Nov. 14, 2025

  211. [219]

    A Machine Learning Approach for Detecting Third-Party Trackers on the Web,

    Q. Wu, Q. Liu, Y . Zhang, P. Liu, and G. Wen, “A Machine Learning Approach for Detecting Third-Party Trackers on the Web,” inEu- ropean Symposium on Research in Computer Security (ESORICS), 2016, pp. 238–258

  212. [220]

    TrackerDetector: A system to detect third-party trackers through machine learning,

    Q. Wu, Q. Liu, Y . Zhang, and G. Wen, “TrackerDetector: A system to detect third-party trackers through machine learning,”Computer Networks, vol. 91, pp. 164–173, 2015

  213. [221]

    Web Tracking Site Detection Based on Temporal Link Analysis,

    A. Yamada, H. Masanori, and Y . Miyake, “Web Tracking Site Detection Based on Temporal Link Analysis,” inIEEE International Conference on Advanced Information Networking and Applications Workshops (WAINA), 2010, pp. 626–631

  214. [222]

    Modeling and Discovering Vulnerabilities with Code Property Graphs,

    F. Yamaguchi, N. Golde, D. Arp, and K. Rieck, “Modeling and Discovering Vulnerabilities with Code Property Graphs,” inIEEE Symposium on Security and Privacy (S&P), 2014, pp. 590–604

  215. [223]

    Adhere: Automated Detection and Repair of Intrusive Ads,

    Y . Yan, Y . Zheng, X. Liu, N. Medvidovic, and W. Wang, “Adhere: Automated Detection and Repair of Intrusive Ads,” inInternational Conference on Software Engineering (ICSE), 2023, pp. 486–498

  216. [224]

    WtaGraph: Web Tracking and Advertising Detection using Graph Neural Networks,

    Z. Yang, W. Pei, M. Chen, and C. Yue, “WtaGraph: Web Tracking and Advertising Detection using Graph Neural Networks,” [Online]. Available: https://zenodo.org/records/5166790, accessed: Nov. 14, 2025

  217. [225]

    WTAGRAPH: Web Tracking and Advertising Detection us- ing Graph Neural Networks,

    ——, “WTAGRAPH: Web Tracking and Advertising Detection us- ing Graph Neural Networks,” inIEEE Symposium on Security and Privacy (S&P), 2022, pp. 1540–1557

  218. [226]

    A Comparative Measurement Study of Web Tracking on Mobile and Desktop Environments,

    Z. Yang and C. Yue, “A Comparative Measurement Study of Web Tracking on Mobile and Desktop Environments,”Privacy Enhancing Technologies Symposium (PETS), pp. 24–44, 2020

  219. [227]

    Effectively Protect Your Privacy: Enabling Flexible Privacy Control on Web Tracking,

    S. Yu, D. V . Vargas, and K. Sakurai, “Effectively Protect Your Privacy: Enabling Flexible Privacy Control on Web Tracking,” inIn- ternational Symposium on Computing and Networking (CANDAR), 2017, pp. 533–536

  220. [228]

    Tracking the Trackers,

    Z. Yu, S. Macbeth, K. Modi, and J. M. Pujol, “Tracking the Trackers,” inThe ACM Web Conference (WWW), 2016, p. 121–132

  221. [229]

    AST-Trans: De- tecting Web Tracking using Transformer-based Deep Learning with Abstract Syntax Tree ,

    Y . Yuan, Z. Wei, L. Liu, S. Chen, and J. Su, “ AST-Trans: De- tecting Web Tracking using Transformer-based Deep Learning with Abstract Syntax Tree ,” inInternational Performance, Computing, and Communications Conference (IPCCC), 2024, pp. 1–7

  222. [230]

    Available: https://www.zenodo.org/, accessed: Jul

    Zenodo, “Zenodo,” [Online]. Available: https://www.zenodo.org/, accessed: Jul. 27, 2025

  223. [231]

    Agent Security Bench (ASB): Formalizing and Benchmarking Attacks and Defenses in LLM-based Agents,

    H. Zhang, J. Huang, K. Mei, Y . Yao, Z. Wang, C. Zhan, H. Wang, and Y . Zhang, “Agent Security Bench (ASB): Formalizing and Benchmarking Attacks and Defenses in LLM-based Agents,” inIn- ternational Conference on Learning Representations (ICLR), 2025

  224. [232]

    “It’s a Fair Game

    Z. Zhang, M. Jia, H.-P. H. Lee, B. Yao, S. Das, A. Lerner, D. Wang, and T. Li, ““It’s a Fair Game”, or Is It? Examining How Users Navigate Disclosure Risks and Benefits When Using LLM-Based Conversational Agents,” inConference on Human Factors in Com- puting Systems (CHI), 202...

  225. [233]

    FProbe: The Flow-Centric Detection and a Large-Scale Measurement of Browser Fingerprinting,

    R. Zhao, “FProbe: The Flow-Centric Detection and a Large-Scale Measurement of Browser Fingerprinting,” inInternational Confer- ence on Computer Communications and Networks (ICCCN), 2023, pp. 1–10

  226. [234]

    Traffic-Based Automatic Detection of Browser Fingerprinting,

    R. Zhao, E. Chow, and C. Li, “Traffic-Based Automatic Detection of Browser Fingerprinting,” inInternational Conference on Security and Privacy in Communication Networks (SecureComm), 2019, pp. 365–385

  227. [235]

    Website Data Transparency in the Browser,

    S. Zimmeck, D. Goldelman, O. Kaplan, L. Brown, J. Casler, J. Jean-Charles, J. Champeau, and H. Harkous, “Website Data Transparency in the Browser,”Privacy Enhancing Technologies Symposium (PETS), pp. 211–234, 2024

  228. [236]

    Browser Fingerprinting Identification Using In- cremental Clustering Algorithm Based on Autoencoder,

    F. Zou and H. Zhai, “Browser Fingerprinting Identification Using In- cremental Clustering Algorithm Based on Autoencoder,” inIEEE Int Conf on High Performance Computing & Communications (HPCC), 2021, pp. 525–532. Appendix A. Open Science We publicly release all artifacts that ...

  229. [237]

    The methodology, taxonomy, and systematization are generally detailed and provide a good overview of the ecosystem

    The paper undertakes a comprehensive review of papers on web tracking detection research. The methodology, taxonomy, and systematization are generally detailed and provide a good overview of the ecosystem

  230. [238]

    The paper reproduces some of the papers and provides some suggestions on artifact issues, which make sense

  231. [239]

    The ethics considerations are detailed and should be helpful to authors of SoK papers in general

Pith tools

Reviewed May 8, 2026 · model on record in the stance chip above.