Fingerprinting and Tracing Shadows: The Development and Impact of Browser Fingerprinting on Digital Privacy

Alexander Lawall

REVIEW 5 major objections 5 minor 53 references

Fingerprinting and Tracing Shadows: The Development and Impact of Browser Fingerprinting on Digital Privacy

T0 review · 5 major / 5 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read This paper argues that browser fingerprinting is a sophisticated, growing method for identifying and tracking users without cookies, operating invisibly and largely outside current privacy law.

desk verdict A competent but conventional survey of browser fingerprinting whose quantitative growth claim rests on shaky, partly uncited numbers; useful as an educational overview, not as a research contribution. read the letter →

arxiv 2411.12045 v1 pith:LNZSYSYQ submitted 2024-11-18 cs.CR cs.AIcs.CY

Alexander Lawall This is my paper · ORCID

classification cs.CRcs.AIcs.CY

keywords browserfingerprintingdeviceonlinetrackingdigitalprivacycanvasWebGLAudioGDPR

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

Browser fingerprinting identifies and tracks users by collecting dozens of browser and device characteristics — screen size, installed fonts, GPU vendor, audio processing quirks, network interfaces — and combining them into a stable identifier that requires no stored cookie and no user consent. The paper, a survey of the field, tries to establish that this technique has grown from a niche research curiosity into a widespread tracking practice, with fingerprinting scripts detected on nearly 10% of the top 100,000 sites in 2021 versus 5.5% using canvas fingerprinting in 2014. It also argues that no single signal is enough on its own: the power lies in the combinatorial use of passive and active methods, which produces fingerprints that resist deletion, incognito mode, and simple browser changes. If the paper is right, existing privacy tools and EU data protection law give users a false sense of security, because fingerprinting operates in the background, is hard to detect, and falls into a legal grey area under the GDPR.

What carries the argument

The central object is the browser fingerprint itself: a short identifier produced by hashing together many small signals a browser reveals, both passively (HTTP headers, CSS queries) and actively (JavaScript-driven canvas rendering, WebGL GPU queries, Web Audio waveforms, WebRTC interface enumeration). The paper evaluates every technique against three properties — uniqueness, stability, and entropy — because a fingerprint that changes too often cannot track, while one that is too common cannot identify. The carrying mechanism is combinatorial: individually weak signals become a strong identifier when merged, which is why the survey treats technique integration rather than any single method as the real tracking engine.

What would settle it

A longitudinal crawl that applies one consistent fingerprinting-script detector to the same set of the top 100,000 sites under 2014-style conditions, again under 2021 conditions, and today would settle the growth claim: if the detected proportion does not rise, or if the 2014 and 2021 numbers cannot both be reproduced with the same methodology, the paper's central trajectory is wrong.

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Extended reading notes

Core claim

The paper's central claim is that browser fingerprinting is a sophisticated method for identifying and tracking users online without traditional mechanisms like cookies, and that its importance is growing as cookie-based tracking is restricted. The analysis catalogues ten families of techniques and evaluates each on uniqueness, stability, entropy, privacy impact, and available defenses. Its empirical spine is a set of secondary measurements: 5.5% of the top 100,000 sites used canvas fingerprinting in 2014, nearly 10% used fingerprinting scripts in 2021, and post-GDPR measurements put fingerprinting scripts on 68.8% of the top 10,000 sites. From these, the paper concludes that fingerprinting is an evolving, multi-dimensional profiling system whose combined techniques are increasingly resistant to countermeasures, and that users are often tracked without knowledge or consent.

Load-bearing premise

The paper relies on prevalence and accuracy figures reported by other studies — 5.5% in 2014, nearly 10% in 2021, 68.8% after the GDPR, and 98% WebGPU classification — being measured in comparable ways and reported correctly; if those numbers are not reliable, the conclusion that fingerprinting is growing and highly effective loses its empirical support.

Editorial extensions

If this is right

As third-party cookies are blocked by browsers and restricted by regulations, fingerprinting becomes the default fallback for online tracking rather than a marginal technique.
Users who delete cookies or browse in incognito mode remain identifiable, because the fingerprint is derived from the device and browser configuration, not from stored state.
Privacy regulations like the GDPR, which are built around consent for stored data, do not clearly cover background fingerprinting, leaving an enforcement gap.
Defenses that randomize a single signal are unlikely to work; the survey's Table I implies that only coordinated changes across many high-entropy dimensions can reduce identifiability.
Newer techniques, especially machine-learning side-channel analysis of CPU, cache, and GPU behavior, may track users in ways current anti-fingerprinting browsers do not mitigate.

Reading between the lines

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

Because the growth numbers come from different studies with different methodologies, a fair test of the paper's trajectory would be one consistent crawl of the same top-site set across 2014, 2021, and now; the paper leaves that comparison implicit.
The 98% WebGPU classification figure the paper cites suggests that as WebGPU is adopted, current defenses that spoof WebGL renderer strings will need a hardware-level randomization layer, an extension the paper only gestures at.
The paper's 'blending in with the masses' recommendation implies a testable design goal: a privacy browser should aim to match a majority configuration across all fingerprint dimensions simultaneously rather than maximally reduce information per dimension.
If GDPR enforcement begins to treat fingerprinting as personal data collection requiring consent, the economic incentive behind the technique would weaken; the paper notes the legal grey area but does not model how enforcement would change prevalence.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

You asked for my read on arXiv:2411.12045. The bottom line: it is a clean, readable survey of browser fingerprinting techniques, but it contains no new research, and its main quantitative trend claim is built on shaky comparisons. The stress-test note is right: the 5.5% (2014, canvas-only) vs. 10% (2021, any script) comparison is not apples-to-apples, and the 68.8% post-GDPR figure in Section IV has no citation at all. Those two numbers do a lot of work in the paper's argument that fingerprinting is rapidly growing, and they cannot be verified as stated. The direction of the claim is probably correct—other cited work and the broader literature support rising fingerprinting use—but the specific figures should not be presented as if they were measured on a comparable basis.

What the paper does well: the technique-by-technique organization is sensible, and Table I gives a handy qualitative overview of uniqueness, stability, entropy, impact, and defenses. The practical advice in Section V.B (blend in, pick browsers with large user bases, limit extensions) is sound and well-grounded in the cited literature. The references hit the key works: Eckersley, Acar, Laperdrix, Iqbal, Englehardt, and the WebRTC and audio fingerprinting papers. As an introduction for someone new to the area, it is honest and mostly accurate.

Where it is soft, beyond the numbers: the abstract promises an analysis of entropy and uniqueness, but the paper mostly reports other people's findings without a methodology of its own. Table I is presented without criteria for how the qualitative ratings were assigned. The WebGPU 98% accuracy claim comes from a heise article rather than the primary paper, which is fine for a survey if attributed, but it is stated as fact. None of these are fatal—they are the standard limitations of a broad survey—but they mean the paper's analytic value is modest.

Who is this for? An undergraduate or a non-specialist looking for a single overview. A security researcher will already know most of this, and the Laperdrix et al. survey is more rigorous and current. I would not cite it in my own work, and I would not bring it to a reading group focused on research. That said, it is a serious, good-faith effort—not sloppy or misleading in its general thrust—so if it appears in a teaching-oriented venue or as a preprint, that seems appropriate. For a top security venue, a desk reject is the right call; the evidentiary weaknesses in the prevalence claims keep it below the bar for referee time.

Referee Report

5 major / 5 minor

Summary. The paper surveys browser fingerprinting techniques, their development, and their privacy implications. It provides a taxonomy of methods (HTTP headers, plugin enumeration, canvas, WebGL, audio, font, screen, WebRTC, CSS, JavaScript attributes, and machine-learning side-channels), a comparison table, a discussion of legal and regulatory issues, and practical recommendations for users. The central claim is that browser fingerprinting is a growing and increasingly sophisticated threat to privacy, based on a comparison of prevalence studies and a set of qualitative assessments of each technique.

Significance. If the central claim is supported, the paper usefully consolidates a broad body of primary research into an accessible overview, with the comparison table and the systematic listing of advantages and disadvantages constituting a practical reference for readers outside the fingerprinting subfield. The paper also gives explicit credit to prior work rather than claiming original measurements, and its recommendations (e.g., choosing specific browsers, limiting extensions) are concrete and actionable. However, the paper makes no original measurements or derivations; its value lies in synthesis and exposition. The main risk is that the growth argument and several quantitative statements rest on secondary sources whose comparability and accuracy are not critically examined, and the qualitative table lacks any stated methodology. These issues are load-bearing for the paper's core conclusion.

major comments (5)

[Section II.B] The claim of 'an almost doubling of usage over seven years' rests on comparing a 2014 study that measured canvas fingerprinting only (5.5% of the top 100,000 sites) with a 2021 study that detected any fingerprinting script (nearly 10% of the Alexa Top 100,000 sites). These metrics are not commensurable: the 2014 figure is a canvas-specific detection, whereas the 2021 figure aggregates multiple fingerprinting methods, and the crawler methodology, detection heuristics, and site-ranking snapshot likely differ. The paper calls the two studies 'similar' without justification. This comparison is the primary quantitative support for the paper's central 'growing' claim, so it must be either corrected with comparable figures or explicitly hedged as non-comparable evidence.
[Section IV] The statement 'Post-GDPR, fingerprinting scripts increased to 68.8% of the top 10,000 sites' appears without any citation or source. This is a specific, quantitative, and load-bearing statistic for the paper's discussion of regulatory impact, and it is unverifiable as written. The authors must either provide a source or delete the sentence.
[Section III.D.2 and Table I] The paper reports a '98% accuracy in 150 milliseconds' for WebGPU-based classification on the authority of a heise article (reference [25]), and uses this as evidence of the growing sophistication of fingerprinting. This is a secondary-source attribution for a technical claim that would require a peer-reviewed primary source or at least critical discussion of the heise article's methodology. Similarly, Table I assigns qualitative ratings (e.g., 'Very High' uniqueness, 'Very High' impact) to each technique, but no methodology, rubric, or source is given for these ratings. The table appears to be the paper's own synthesis, yet it is presented as if it were an objective analysis; this needs an explicit statement of how the ratings were derived and, ideally, citations for each rating.
[Abstract and Section V.A] The abstract claims that the paper 'analyzes the entropy and uniqueness of the collected data,' and the conclusion says 'The analysis highlighted that browser fingerprinting poses a complex challenge.' However, the paper presents no original entropy or uniqueness analysis; it only cites previous studies (e.g., Eckersley, AmIUnique) without aggregating or re-analyzing their data. This overstates the paper's analytic contribution and should be rephrased to describe the paper as a survey that reports and organizes existing findings.
[Section II.B and Section IV] The paper's overall argument that fingerprinting is 'growing' relies on the prevalence comparison discussed above and on the uncited 68.8% figure. If these are removed or corrected, the conclusion that fingerprinting is growing can only be supported by qualitative/anecdotal evidence. The authors should either strengthen this evidence with a rigorous, citable aggregation of prevalence studies or soften the claim to indicate that fingerprinting remains a significant and evolving threat without asserting an unverified temporal trend.

minor comments (5)

[Section IV] The phrase 'as shown by technologies like BrFast and private, passive user recognition methods' mentions BrFast without a reference; either provide a citation or remove the example.
[Section III.D.2] The sentence 'allowing for classifications with up to 98% accuracy in 150 milliseconds, a reduction from the 8 seconds WebGL took' is ambiguous: it is unclear whether 'reduction' refers to the time improvement from WebGL to WebGPU or to a drop in accuracy; please clarify.
[Table I] The table has no column note or footnote explaining the meaning of 'Low', 'Moderate', 'High', and 'Very High' beyond the words themselves; adding a short legend would improve interpretability.
[Section III.J.2] The statement 'Mowery et al. demonstrated that these differences are measurable' is vague because reference [1] is about canvas fingerprinting, not JavaScript attribute differences; a more specific citation or a clearer explanation is needed.
[Throughout] The text contains several typos and minor grammatical issues (e.g., 'The paper is structured as follows: Section I introduces browser fingerprinting and its privacy implications. In Section II, the theoretical background explains...' is fine, but other sentences such as 'the process to occur secretly and without consent [1, p. 1]' read awkwardly). A careful proofreading pass is recommended.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the paper is a survey that derives no quantitative results and makes no original predictions, so its claims cannot reduce to their own inputs.

full rationale

This paper is a literature review and taxonomy of browser fingerprinting techniques. It contains no original derivation chain, no fitted parameters, no equations, and no prediction that is subsequently validated against data. The conclusions, such as 'Browser fingerprinting is a sophisticated method for identifying and tracking users online without traditional methods like cookies,' are summaries of external studies rather than outputs of a model trained on those studies. The numerical claims about prevalence (e.g., the 2014 canvas-only 5.5% vs. the 2021 all-method ~10% comparison, and the uncited 68.8% post-GDPR figure) are potentially problematic for verifiability and comparability, but those are correctness or sourcing concerns, not circular reasoning. No self-citation is load-bearing: the author cites no prior work of their own, and no 'uniqueness theorem' or ansatz is imported from author-controlled sources. The paper is self-contained as a survey and its claims rest on external evidence, so there is no circularity.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

As a review paper, the work introduces no free parameters or invented entities. It relies on the validity of external studies and on subjective qualitative ratings without a transparent methodology.

assumptions (3)

domain assumption The cited studies' methods and results are accurate and generalizable to the broader web.
The paper's conclusions about prevalence and effectiveness rely on measurements from studies of specific website sets and participant groups, which are assumed to generalize.
ad hoc to paper The qualitative ratings in Table I (e.g., High, Very High uniqueness) are meaningful despite lacking a defined methodology or source.
The table presents subjective assessments without explanation, and these ratings are used in the discussion to support claims about the threat level.
domain assumption The paper's selection of techniques is representative of all browser fingerprinting methods.
The paper states it covers only the most common or established methods, but it does not systematically justify this choice.

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Cite this review

Pith. "Pith review of Fingerprinting and Tracing Shadows: The Development and Impact of Browser Fingerprinting on Digital Privacy." pith.science (2026). https://pith.science/paper/LNZSYSYQ

@misc{pith2026241112045,
  author       = {Pith},
  title        = {Pith review of: Fingerprinting and Tracing Shadows: The Development and Impact of Browser Fingerprinting on Digital Privacy},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/LNZSYSYQ}},
  note         = {Machine review of arXiv:2411.12045}
}

read the original abstract

Browser fingerprinting is a growing technique for identifying and tracking users online without traditional methods like cookies. This paper gives an overview by examining the various fingerprinting techniques and analyzes the entropy and uniqueness of the collected data. The analysis highlights that browser fingerprinting poses a complex challenge from both technical and privacy perspectives, as users often have no control over the collection and use of their data. In addition, it raises significant privacy concerns as users are often tracked without their knowledge or consent.

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

53 extracted references · 49 canonical work pages

[25]

M. Mantel. (2022) Browser-Fingerprinting: PCs, Smart phones & Co. lassen sich ¨ uber die GPU tracken. Accessed: 2024-09-27. [O nline]. Available: https://www.heise.de/news/Browser-Fingerp rinting- PCs- Smartphones-Co-lassen-sich-ueber-die-GPU-tracken- 63 45233.html

work page 2022
[1]

Pixel perfect: Fingerprintin g canvas in HTML5,

K. Mowery and H. Shacham, “Pixel perfect: Fingerprintin g canvas in HTML5,” Proceedings of W2SP , vol. 2012, 2012

work page 2012
[2]

Szymielewicz and B

K. Szymielewicz and B. Budington. (2018) The GDPR and Browser Fingerprinting: How It Changes the Game for the Sneakiest Web Trackers. Accessed: 2024-09-27. [Online ]. Available: https://www.eff.org/de/deeplinks/2018/06/ gdpr-and-browser- ﬁngerprinting- how-it-changes-game-sneakiest- web-tra ckers

work page 2018
[3]

A Survey of Browser Fingerprint Research and Application,

D. Zhang, J. Zhang, Y . Bu, B. Chen, C. Sun, and T. Wang, “A Survey of Browser Fingerprint Research and Application,” Wireless Communications and Mobile Computing , vol. 2022, no. 1, p. 3363335,

work page 2022
[4]

How unique is your web browser?

P . Eckersley, “How unique is your web browser?” in Privacy Enhancing Technologies, M. J. Atallah and N. J. Hopper, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010, pp. 1–18

work page 2010
[5]

Englehardt and A

S. Englehardt and A. Edelstein. (2021) Firefox 85 Cracks Down on Supercookies. Accessed: 2024-09-27. [Online]. Available : https://blog. mozilla.org/security/2021/01/26/supercookie- protections/

work page 2021
[6]

Woollacott

E. Woollacott. (2021) Browser ﬁngerprinting more preva lent on the web now than ever before. Accessed: 2024-09-27. [Online]. A vail- able: https://portswigger.net/daily-swig/browser-ﬁng erprinting- more- prevalent- on-the-web-now-than-ever-before-research

work page 2021
[7]

R. Koch. (2019) Cookies, the GDPR, and the ePrivacy Direc tive. Accessed: 2024-09-27. [Online]. Available: https://gdpr .eu/cookies/

work page 2019

Show all 53 references

[8]

Fingerprinting t he Fingerprint- ers: Learning to Detect Browser Fingerprinting Behaviors,

U. Iqbal, S. Englehardt, and Z. Shaﬁq, “Fingerprinting t he Fingerprint- ers: Learning to Detect Browser Fingerprinting Behaviors, ” in 2021 IEEE Symposium on Security and Privacy (SP) . IEEE, 05 2021, pp. 1143–1161

2021
[9]

G. Acar. (2014) Browser Fingerprinting and the Online- Tracking Arms Race. Accessed: 2024-09-27. [Online]. Avail - able: https://www.esat.kuleuven.be/cosic/news/the-we b-never-forgets- persistent- tracking- mechanisms-in-the-wild/

2014
[10]

Request header,

“Request header,” accessed: 2024-09-27. [Online]. Av ailable: https:// developer.mozilla.org/en-US/docs/Glossary/Request header

2024
[11]

HTTP/2 ﬁngerprinting: A relatively-unknown method f or web ﬁngerprinting,

“HTTP/2 ﬁngerprinting: A relatively-unknown method f or web ﬁngerprinting,” accessed: 2024-09-27. [Online]. Availab le: https:// lwthiker.com/networks/2022/06/17/http2-ﬁngerprintin g.html

2024
[12]

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,” in 2016 IEEE Symposium on Security and Privacy (SP) , 2016, pp. 878– 894

2016
[13]

User-Agent,

“User-Agent,” accessed: 2024-09-27. [Online]. Avail able: https:// developer.mozilla.org/en-US/docs/Web/HTTP/Headers/User-Agent

2024
[14]

B. Wolford. (2024) What are the GDPR consent requiremen ts? Accessed: 2024-09-27. [Online]. Available: https://gdpr .eu/gdpr- consent-requirements/

2024
[15]

Navigator: plugins property,

“Navigator: plugins property,” accessed: 2024-09-27 . [On- line]. Available: https://developer.mozilla.org/en-US /docs/Web/API/ Navigator/plugins

2024
[16]

Extension Detector,

“Extension Detector,” accessed: 2024-09-27. [Online ]. Available: https:// github.com/z0ccc/extension- detector

2024
[17]

How ad blockers can be used for browser ﬁngerprinting,

“How ad blockers can be used for browser ﬁngerprinting, ” accessed: 2024-09-27. [Online]. Available: https://ﬁnge rprint.com/ blog/ad-blocker-ﬁngerprinting/

2024
[18]

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,” in 27th Annual Network and Distributed System Security Symposium, NDSS 20 20, San Diego, California, USA, February 23-26, 2020 . The Internet Society, 2020

2020
[19]

CanvasBlocker,

“CanvasBlocker,” accessed: 2024-09-27. [Online]. Av ailable: https:// github.com/kkapsner/CanvasBlocker

2024
[20]

WebGL: 2D and 3D graphics for the web,

“WebGL: 2D and 3D graphics for the web,” accessed: 2024- 09-27. [Online]. Available: https://developer.mozilla.org/en -US/docs/Web/API/ WebGL API

2024
[21]

A Comparative Study on Analyses of Brow ser Finger- printing,

T. Stephenson, “A Comparative Study on Analyses of Brow ser Finger- printing,” Ph.D. dissertation, Wesleyan University, 2023

2023
[22]

[Online]

“WebKit,” accessed: 2024-09-27. [Online]. Available: https://github.com/WebKit/WebKit/commit/ ae710d34c23858295b385e3f95ad7f6edd29f9d7

2024
[23]

Hardware Fin gerprinting Using HTML5,

G. Nakibly, G. Shelef, and S. Y udilevich, “Hardware Fin gerprinting Using HTML5,” arXiv preprint arXiv:1503.01408 , 03 2015

2015 arXiv
[24]

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

Y . Cao, S. Li, and E. Wijmans, “(Cross-)Browser Fingerp rinting via OS and Hardware Level Features,” in Network and Distributed System Security Symposium , 2017

2017
[26]

DRAWNAPART: A Device Identiﬁcation Techn ique based on Remote GPU Fingerprinting,

Laor et al., “DRAWNAPART: A Device Identiﬁcation Techn ique based on Remote GPU Fingerprinting,” ArXiv, vol. abs/2201.09956,
[27]

A Web Browser Fingerpri nting Method Based on the Web Audio API,

J. S. Queiroz and E. L. Feitosa, “A Web Browser Fingerpri nting Method Based on the Web Audio API,” Comput. J. , vol. 62, pp. 1106–1120, 2019. [Online]. Available: https://api.seman ticscholar.org/ CorpusID:86644316

2019
[28]

Available: https://api.semanticscholar .org/CorpusID: 246276013

[Online]. Available: https://api.semanticscholar .org/CorpusID: 246276013
[29]

Hiding in the Crowd: an Analysis of the Effectiveness of Browser Fingerprinting at Large Scale,

A. G´ omez-Boix, P . Laperdrix, and B. Baudry, “Hiding in the Crowd: an Analysis of the Effectiveness of Browser Fingerprinting at Large Scale,” in Proceedings of the 2018 W orld Wide W eb Conference , ser. WWW ’18. Republic and Canton of Geneva, CHE: International World Wide ...

2018 doi
[30]

A Study of Feasibility and Di versity of Web Audio Fingerprints,

S. Chalise and P . V adrevu, “A Study of Feasibility and Di versity of Web Audio Fingerprints,” arXiv preprint arXiv:2107.14201 , 2021

2021 arXiv
[31]

Local Font Access API,

“Local Font Access API,” accessed: 2024-09-27. [Onlin e]. Avail- able: https://developer.mozilla.org/en-US/docs/Web/A PI/Local Font Access API

2024
[32]

Online Tracking: A 1-m illion-site Measurement and Analysis,

S. Englehardt and A. Narayanan, “Online Tracking: A 1-m illion-site Measurement and Analysis,” in Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security , ser. CCS ’16. New Y ork, NY , USA: Association for Computing Machinery, 201 6, p. 1388–1401...

2016
[33]

WebRTC: your privacy is at ri sk,

A. Reiter and A. Marsalek, “WebRTC: your privacy is at ri sk,” in Proceedings of the Symposium on Applied Computing , ser. SAC ’17. New Y ork, NY , USA: Association for Computing Machinery, 201 7, p. 664–669. [Online]. Available: https://doi.org/10.1145/ 3019612.3019844
[34]

Bro wser Fingerprinting: A Survey,

P . Laperdrix, N. Bielova, B. Baudry, and G. Avoine, “Bro wser Fingerprinting: A Survey,” ACM Trans. W eb, vol. 14, no. 2, apr 2020. [Online]. Available: https://doi.org/10.1145/3386040

2020 doi
[35]

Web-based Fingerprintin g Techniques,

V . Bernardo and D. Domingos, “Web-based Fingerprintin g Techniques,” in Proceedings of the 13th International Joint Conference on E-Business and Telecommunications , ser. ICETE 2016. Setubal, PRT: SCITEPRESS - Science and Technology Publications, Lda , 2016, p. 271–282. [Onli...

2016
[36]

DetectRTC,

“DetectRTC,” accessed: 2024-09-27. [Online]. Availa ble: https://github. com/muaz-khan/DetectRTC

2024
[37]

Why Johnny Ca n’t Browse in Peace: On the Uniqueness of Web Browsing History Patterns ,

L. Olejnik, C. Castelluccia, and A. Janc, “Why Johnny Ca n’t Browse in Peace: On the Uniqueness of Web Browsing History Patterns ,” 12th Privacy Enhancing Technologies Symposium (PETS 2012) , 07 2012. [Online]. Available: https://petsymposium.org/2012/pa pers/hotpets12-4- johnny.pdf

2012
[38]

A Survey on Web Tracking: Mechanisms, Implications, and Defe nses,

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

2017
[39]

CSS-Fingerprint,

“CSS-Fingerprint,” accessed: 2024-09-27. [Online]. Available: https:// github.com/OliverBrotchie/CSS-Fingerprint

2024
[40]

Web Browse r Finger- printing Using Only Cascading Style Sheets,

N. Takei, T. Saito, K. Takasu, and T. Y amada, “Web Browse r Finger- printing Using Only Cascading Style Sheets,” in 2015 10th International Conference on Broadband and Wireless Computing, Communication and Applications (BWCCA) , 2015, pp. 57–63

2015
[41]

Investigate impact of ﬁngerprinting via getClientRe cts(),

“Investigate impact of ﬁngerprinting via getClientRe cts(),” accessed: 2024-09-27. [Online]. Available: https://gitlab.torpro ject.org/tpo/ applications/tor-browser/-/issues/18500

2024
[42]

Advanced Tor Browser Fingerprinting,

“Advanced Tor Browser Fingerprinting,” accessed: 202 4-09-
[43]

Available: http://jcarlosnorte.com/secur ity/2016/03/06/ advanced- tor-browser-ﬁngerprinting.html

[Online]. Available: http://jcarlosnorte.com/secur ity/2016/03/06/ advanced- tor-browser-ﬁngerprinting.html

2016
[44]

High precision timing,

“High precision timing,” accessed: 2024-09-27. [Onli ne]. Avail- able: https://developer.mozilla.org/en-US/docs/Web/A PI/Performance API/High precision timing

2024
[45]

Machine Learning-Assisted Website Fingerpr inting At- tacks with Side-Channel Information: A Comprehensive Anal ysis and Characterization,

H. Wang, H. Sayadi, A. Sasan, P . D. Sai Manoj, S. Rafatira d, and H. Homayoun, “Machine Learning-Assisted Website Fingerpr inting At- tacks with Side-Channel Information: A Comprehensive Anal ysis and Characterization,” in 2021 22nd International Symposium on Quality Electron...

2021
[46]

WebAssembly,

“WebAssembly,” accessed: 2024-09-27. [Online]. Avai lable: https:// developer.mozilla.org/en-US/docs/WebAssembly

2024
[47]

Too little too late: can w e control browser ﬁngerprinting?

N. Al-Fannah and C. Mitchell, “Too little too late: can w e control browser ﬁngerprinting?” Journal of Intellectual Capital , vol. ahead-of- print, 01 2020

2020
[48]

Browser Fingerprinting und das TDDDG: Erlaubt oder ni cht? [Browser Fingerprinting and the TDDDG: Allowed or not?],

“Browser Fingerprinting und das TDDDG: Erlaubt oder ni cht? [Browser Fingerprinting and the TDDDG: Allowed or not?],” accessed: 2024-09-

2024
[49]

Available: https://dr-dsgvo.de/browser-ﬁ ngerprinting- und- das-ttdsg/

[Online]. Available: https://dr-dsgvo.de/browser-ﬁ ngerprinting- und- das-ttdsg/
[50]

Lo ng-Term Observation on Browser Fingerprinting: Users’ Trackabili ty and Per- spective,

G. Pugliese, C. Riess, F. Gassmann, and Z. Benenson, “Lo ng-Term Observation on Browser Fingerprinting: Users’ Trackabili ty and Per- spective,” Proceedings on Privacy Enhancing Technologies , vol. 2020, pp. 558–577, 05 2020

2020
[52]

Goodbye Tracking? Impact of iOS App Tracking Transparency and Privacy Labels,

K. Kollnig, A. Shuba, M. V an Kleek, R. Binns, and N. Shadb olt, “Goodbye Tracking? Impact of iOS App Tracking Transparency and Privacy Labels,” in Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency , ser. FAccT ’22. New Y ork, NY , USA: Assoc...

2022
[53]

Fa shion Faux Pas: Implicit Stylistic Fingerprints for Bypassing Browse rs’ Anti- Fingerprinting Defenses,

X. Lin, F. Araujo, T. Taylor, J. Jang, and J. Polakis, “Fa shion Faux Pas: Implicit Stylistic Fingerprints for Bypassing Browse rs’ Anti- Fingerprinting Defenses,” in 2023 IEEE Symposium on Security and Privacy (SP) , 2023, pp. 987–1004

2023
[2022]

Available: https://onlinelibrary.wiley .com/doi/abs/10

[Online]. Available: https://onlinelibrary.wiley .com/doi/abs/10. 1155/2022/3363335

2022

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.

[1] [25]

M. Mantel. (2022) Browser-Fingerprinting: PCs, Smart phones & Co. lassen sich ¨ uber die GPU tracken. Accessed: 2024-09-27. [O nline]. Available: https://www.heise.de/news/Browser-Fingerp rinting- PCs- Smartphones-Co-lassen-sich-ueber-die-GPU-tracken- 63 45233.html

work page 2022

[2] [1]

Pixel perfect: Fingerprintin g canvas in HTML5,

K. Mowery and H. Shacham, “Pixel perfect: Fingerprintin g canvas in HTML5,” Proceedings of W2SP , vol. 2012, 2012

work page 2012

[3] [2]

Szymielewicz and B

K. Szymielewicz and B. Budington. (2018) The GDPR and Browser Fingerprinting: How It Changes the Game for the Sneakiest Web Trackers. Accessed: 2024-09-27. [Online ]. Available: https://www.eff.org/de/deeplinks/2018/06/ gdpr-and-browser- ﬁngerprinting- how-it-changes-game-sneakiest- web-tra ckers

work page 2018

[4] [3]

A Survey of Browser Fingerprint Research and Application,

D. Zhang, J. Zhang, Y . Bu, B. Chen, C. Sun, and T. Wang, “A Survey of Browser Fingerprint Research and Application,” Wireless Communications and Mobile Computing , vol. 2022, no. 1, p. 3363335,

work page 2022

[5] [4]

How unique is your web browser?

P . Eckersley, “How unique is your web browser?” in Privacy Enhancing Technologies, M. J. Atallah and N. J. Hopper, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010, pp. 1–18

work page 2010

[6] [5]

Englehardt and A

S. Englehardt and A. Edelstein. (2021) Firefox 85 Cracks Down on Supercookies. Accessed: 2024-09-27. [Online]. Available : https://blog. mozilla.org/security/2021/01/26/supercookie- protections/

work page 2021

[7] [6]

Woollacott

E. Woollacott. (2021) Browser ﬁngerprinting more preva lent on the web now than ever before. Accessed: 2024-09-27. [Online]. A vail- able: https://portswigger.net/daily-swig/browser-ﬁng erprinting- more- prevalent- on-the-web-now-than-ever-before-research

work page 2021

[8] [7]

R. Koch. (2019) Cookies, the GDPR, and the ePrivacy Direc tive. Accessed: 2024-09-27. [Online]. Available: https://gdpr .eu/cookies/

work page 2019