Browser Finger

HTTP Rotating & Static

  • 200 thousand IPs
  • Locations: US, EU
  • Monthly price: from $39
  • 1 day moneyback guarantee


FingerprintJS – GitHub

FingerprintJS is a browser fingerprinting library that queries browser attributes and computes a hashed visitor identifier from them. Unlike cookies and local storage, a fingerprint stays the same in incognito/private mode and even when browser data is purged.
View Our Demo.
Quick start
Install from CDN

Run this code
Alternatively you can install from NPM to use with Webpack/Rollup/Browserify
npm i @fingerprintjs/fingerprintjs
# or
yarn add @fingerprintjs/fingerprintjs
import FingerprintJS from ‘@fingerprintjs/fingerprintjs’
// Initialize an agent at application startup.
const fpPromise = ();(async () => {
const fp = await fpPromise
const result = await ()
Full documentation
Upgrade to Pro version to get 99. 5% identification accuracy
FingerprintJS Pro is a professional visitor identification service that processes all information server-side and transmits it securely to your servers using server-to-server APIs.
Pro combines browser fingerprinting with vast amounts of auxiliary data (IP addresses, time of visit patterns, URL changes and more) to be able to reliably deduplicate different users that have identical devices, resulting in 99. 5% identification accuracy.
You can try Pro without usage limits for 10 days – no credit card required.
Full product comparison:
Open Source
Core Features
100% Open-sourceyesno1
Standard fingerprint signalsscreen, os, device name✓✓
Advanced fingerprint signalscanvas, audio, fonts✓✓
ID typefingerprintvisitorID2
ID lifetimeseveral weeksmonths/years
ID originclientserver
ID collisionscommonrare
Additional Features
Incognito mode detectionworks in all modern browsers – see our full list of browsers supported–✓
Server-side accuracy increasebased on additional server-side signals, such as TLS crypto support, ipv4/v6 data and others–✓
Query API & realtime Webhooksbuild flexible workflows–✓
Geolocationbased on IP address–✓
Data securityYour infrastructureEncrypted at rest
StorageYour infrastructureUnlimited up to 1 yr
RegionsYour infrastructureHosting in US and EU
ComplianceYour infrastructureGDPR, CCPA compliant3
SLANo SLA99. 9% Uptime
SupportGitHub communitySupport team via email, chat, and call-back within 1 business day
1. Pro uses the open source fingerprinting library as well as proprietary technology for increased accuracy and identifier stability.
2. VisitorIDs, in comparison to fingerprints, include server side techniques, are deduplicated and utilize fuzzy matching to result in a more accurate and stable identifier. Fingerprint hashes rely on an exact match across all browser attributes, making them less stable across > 4 week time intervals.
3. FingerprintJS Pro is GDPR and CCPA compliant as the data processor. You still need to be compliant as the data controller and use the identification for fraud under legitimate interest or ask for user consent.
Pro result example:
“requestId”: “HFMlljrzKEiZmhUNDx7Z”,
“visitorId”: “kHqPGWS1Mj18sZFsP8Wl”,
“visitorFound”: true,
“confidence”: { “score”: 0. 995},
“incognito”: false,
“browserName”: “Chrome”,
“browserVersion”: “92. 0. 4515. 107”,
“os”: “Mac OS X”,
“osVersion”: “10. 15. 6”,
“device”: “Other”,
“ip”: “192. 65. 67. 131”,
“ipLocation”: {
“accuracyRadius”: 100,
“latitude”: 37. 409657,
“longitude”: -121. 965467
//… }}
Live demo
⏱ How to upgrade from Open Source to Pro in 30 seconds
FingerprintJS Pro documentation
▶️ Video: use FingerprintJS Pro to prevent multiple signups
Migrating from v2
Migration guide
V2 documentation
Version policy
See the compatibility policy for the API and visitor identifiers in the version policy guide.
Browser support
The library supports all popular browsers.
See more details and learn how to run the library in old browsers in the browser support guide.
See the contributing guidelines to learn how to start a playground, test and build.
Useful links
Easy to use bot detection in JS, new FingerprintJS project
Device fingerprint - Wikipedia


  • No logs
  • Kill Switch
  • 6 devices
  • Monthly price: $4.92


Device fingerprint – Wikipedia

A device fingerprint or machine fingerprint is information collected about the software and hardware of a remote computing device for the purpose of identification. The information is usually assimilated into a brief identifier using a fingerprinting algorithm. A browser fingerprint is information collected specifically by interaction with the web browser of the device. [1]: 878 [2]: 1 
Device fingerprints can be used to fully or partially identify individual devices even when persistent cookies (and zombie cookies) cannot be read or stored in the browser, the client IP address is hidden, or one switches to another browser on the same device. [3]
This may allow a service provider to detect and prevent identity theft and credit card fraud, [4]: 299 [5][6][7] but also to compile long-term records of individuals’ browsing histories (and deliver targeted advertising[8]: 821 [9]: 9  or targeted exploits[10]: 8 [11]: 547) even when they are attempting to avoid tracking – raising a major concern for internet privacy advocates. [12]
This section needs to be updated. Please help update this article to reflect recent events or newly available information. (March 2020)
Basic web browser configuration information has long been collected by web analytics services in an effort to measure real human web traffic and discount various forms of click fraud. Since its introduction in the late 1990s, client-side scripting has gradually enabled the collection of an increasing amount of diverse information, with some computer security experts starting to complain about the ease of bulk parameter extraction offered by web browsers as early as 2003. [13]
In 2005, researchers at University of California, San Diego showed how TCP timestamps could be used to estimate the clock skew of a device, and consequently to remotely obtain a hardware fingerprint of the device. [14]
In 2010, Electronic Frontier Foundation launched a website where visitors can test their browser fingerprint. [15] After collecting a sample of 470161 fingerprints, they measured at least 18. 1 bits of entropy possible from browser fingerprinting, [16] but that was before the advancements of canvas fingerprinting, which claims to add another 5. 7 bits.
In 2012, Keaton Mowery and Hovav Shacham, researchers at University of California, San Diego, showed how the HTML5 canvas element could be used to create digital fingerprints of web browsers. [17][18]
In 2013, at least 0. 4% of Alexa top 10, 000 sites were found to use fingerprinting scripts provided by a few known third parties. [11]: 546 
In 2014, 5. 5% of Alexa top 10, 000 sites were found to use canvas fingerprinting scripts served by a total of 20 domains. The overwhelming majority (95%) of the scripts were served by AddThis, which started using canvas fingerprinting in January that year, without the knowledge of some of its clients. [19]: 678 [20][17][21][5]
In 2015, a feature to protect against browser fingerprinting was introduced in Firefox version 41, [22] but it has been since left in an experimental stage, not initiated by default. [23]
The same year a feature named Enhanced Tracking Protection was introduced in Firefox version 42 to protect against tracking during private browsing[24] by blocking scripts from third party domains found in the lists published by Disconnect Mobile.
At WWDC 2018 Apple announced that Safari on macOS Mojave “presents simplified system information when users browse the web, preventing them from being tracked based on their system configuration. “[25]
A 2018 study revealed that only one-third of browser fingerprints in a French database were unique, indicating that browser fingerprinting may become less effective as the number of users increases and web technologies convergently evolve to implement fewer distinguishing features. [26]
In 2019, starting from Firefox version 69, Enhanced Tracking Protection has been turned on by default for all users also during non-private browsing. [27] The feature was first introduced to protect private browsing in 2015 and was then extended to standard browsing as an opt-in feature in 2018.
Diversity and stability[edit]
Motivation for the device fingerprint concept stems from the forensic value of human fingerprints.
In order to uniquely distinguish over time some devices through their fingerprints, the fingerprints must be both sufficiently diverse and sufficiently stable. In practice neither diversity nor stability is fully attainable, and improving one has a tendency to adversely impact the other. For example, the assimilation of an additional browser setting into the browser fingerprint would usually increase diversity, but it would also reduce stability, because if a user changes that setting, then the browser fingerprint would change as well. [2]: 11 
A certain degree of instability can be compensated by linking together fingerprints that, although partially different, might probably belong to the same device. This can be accomplished by a simple rule-based linking algorithm (which, for example, links together fingerprints that differ only for the browser version, if that increases with time) or machine learning algorithms. [28]
Entropy is one of several ways to measure diversity.
Sources of identifying information[edit]
Applications that are locally installed on a device are allowed to gather a great amount of information about the software and the hardware of the device, often including unique identifiers such as the MAC address and serial numbers assigned to the machine hardware. Indeed, programs that employ digital rights management use this information for the very purpose of uniquely identifying the device.
Even if they aren’t designed to gather and share identifying information, local applications might unwillingly expose identifying information to the remote parties with which they interact. The most prominent example is that of web browsers, which have been proved to expose diverse and stable information in such an amount to allow remote identification, see § Browser fingerprint.
Diverse and stable information can also be gathered below the application layer, by leveraging the protocols that are used to transmit data. Sorted by OSI model layer, some examples of such protocols are:
OSI Layer 7: SMB, FTP, HTTP, Telnet, TLS/SSL, DHCP[29]
OSI Layer 5: SNMP, NetBIOS
OSI Layer 4: TCP (see TCP/IP stack fingerprinting)
OSI Layer 3: IPv4, IPv6, ICMP, IEEE 802. 11[30]
OSI Layer 2: CDP[31]
Passive fingerprinting techniques merely require the fingerprinter to observe traffic originated from the target device, while active fingerprinting techniques require the fingerprinter to initiate connections to the target device. Techniques that require to interact with the target device over a connection initiated by the latter are sometimes addressed as semi-passive. [14]
Browser fingerprint[edit]
The collection of large amount of diverse and stable information from web browsers is possible thanks for most part to client-side scripting languages, which have been introduced in the late ’90s. Today there are several open-source browser fingerprinting libraries, such as FingerprintJS, ImprintJS, and ClientJS, where FingerprintJS is the most updated and supersedes ImprintJS and ClientJS to a large extent. [32]
Browser version[edit]
Browsers provide their name and version, together with some compatibility information, in the User-Agent request header. [33][34] Being a statement freely given by the client, it shouldn’t be trusted when assessing its identity. Instead, the type and version of the browser can be inferred from the observation of quirks in its behavior: for example, the order and number of HTTP header fields is unique to each browser family[35]: 257 [36]: 357  and, most importantly, each browser family and version differs in its implementation of HTML5, [10]: 1 [35]: 257  CSS[37]: 58 [35]: 256  and JavaScript. [11]: 547, 549-50 [38]: 2 [39][40] Such differences can be remotely tested by using JavaScript. A Hamming distance comparison of parser behaviors has been shown to effectively fingerprint and differentiate a majority of browser versions. [10]: 6 
JavaScript object manipulation is specific to each browser family
Browser family
Property deletion (of navigator object)
Reassignment (of navigator/screen object)
Google Chrome
Mozilla Firefox
Internet Explorer
Browser extensions[edit]
A browser unique combination of extensions or plugins can be added to a fingerprint directly. [11]: 545  Extensions may also modify how any other browser attributes behave, adding additional complexity to the user’s fingerprint. [41]: 954 [42]: 688 [9]: 1131 [43]: 108  Adobe Flash and Java plugins were widely used to access user information before their deprecation. [36]: 3 [11]: 553 [40]
Hardware properties[edit]
User agents may provide system hardware information, such as phone model, in the HTTP header. [43]: 107 [44]: 111 
Properties about the user’s operating system, screen size, screen orientation, and display aspect ratio can be also retrieved by observing with JavaScript the result of CSS media queries. [37]: 59-60 
Browsing history[edit]
The fingerprinter can determine which sites the browser has previously visited within a list it provides, by querying the list using JavaScript with the CSS selector:visited. [45]: 5  Typically, a list of 50 popular websites is sufficient to generate a unique user history profile, as well as provide information about the user’s interests. [45]: 7, 14  However, browsers have since then mitigated this risk. [46]
Font metrics[edit]
The letter bounding boxes differ between browsers based on anti-aliasing and font hinting configuration and can be measured by JavaScript. [47]: 108 
Canvas and WebGL[edit]
Canvas fingerprinting uses the HTML5 canvas element, which is used by WebGL to render 2D and 3D graphics in a browser, to gain identifying information about the installed graphics driver, graphics card, or graphics processing unit (GPU). Canvas-based techniques may also be used to identify installed fonts. [44]: 110  Furthermore, if the user does not have a GPU, CPU information can be provided to the fingerprinter instead.
A canvas fingerprinting script first draws text of specified font, size, and background color. The image of the text as rendered by the user’s browser is then recovered by the ToDataURL Canvas API method. The hashed text-encoded data becomes the user’s fingerprint. [19][18]: 2-3, 6  Canvas fingerprinting methods have been shown to produce 5. 7 bits of entropy. Because the technique obtains information about the user’s GPU, the information entropy gained is “orthogonal” to the entropy of previous browser fingerprint techniques such as screen resolution and JavaScript capabilities. [18]
Hardware benchmarking[edit]
Benchmark tests can be used to determine whether a user’s CPU utilizes AES-NI or Intel Turbo Boost by comparing the CPU time used to execute various simple or cryptographic algorithms. [48]: 588 
Specialized APIs can also be used, such as the Battery API, which constructs a short-term fingerprint based on the actual battery state of the device, [49]: 256  or OscillatorNode, which can be invoked to produce a waveform based on user entropy. [50]: 1399 
A device’s hardware ID, which is a cryptographic hash function specified by the device’s vendor, can also be queried to construct a fingerprint. [44]: 109, 114 
Mitigation methods for browser fingerprinting[edit]
Different approaches exist to mitigate the effects of browser fingerprinting and improve users’ privacy by preventing unwanted tracking, but there is no ultimate approach that can prevent fingerprinting while keeping the richness of a modern web browser. [51]
Offering a simplified fingerprint[edit]
Typical Tor Browser notification of a website attempting a canvas read.
Users may attempt to reduce their fingerprintability by selecting a web browser which minimizes availability of identifying information such as browser fonts, device ID, canvas element rendering, WebGL information, and local IP address. [44]: 117 
As of 2017 Microsoft Edge is considered to be the most fingerprintable browser, followed by Firefox and Google Chrome, Internet Explorer, and Safari. [44]: 114  Among mobile browsers, Google Chrome and Opera Mini are most fingerprintable, followed by mobile Firefox, mobile Edge, and mobile Safari. [44]: 115 
Tor Browser disables fingerprintable features such as the canvas and WebGL API and notify users of fingerprint attempts. [19]
Offering a spoofed fingerprint[edit]
Spoofing some of the information exposed to the fingerprinter (e. g. the user agent) may allow to reduce diversity, [52]: 13  but the contrary could be also achieved if the mismatch between the spoofed information and the real browser information differentiates the user from all the others who do not use such strategy. [11]: 552 
Spoofing the information differently at each site visit, for example by perturbating the sound and canvas rendering with a small amount of random noise, allows to reduce stability. [8]: 820, 823 [53] This technique has been adopted by the Brave browser in 2020. [54]
Blocking scripts[edit]
Blindly blocking client-side scripts served from third-party domains, and possibly also first-party domains (e. by disabling JavaScript or using NoScript) can sometimes render websites unusable. The preferred approach is to block only third-party domains that seem to track people, either because they’re found on a blacklist of tracking domains (the approach followed by most ad blockers) or because the intention of tracking is inferred by past observations (the approach followed by Privacy Badger). [55][20][56][57]
Using multiple browsers[edit]
Different browsers on the same machine would usually have different fingerprints, but if both browsers aren’t protected against fingerprinting, then the two fingerprints could be identified as originating from the same machine. [3][58]
See also[edit]
Anonymous web browsing
Web tracking
Internet privacy
Fingerprint (computing)
Browser security
Browser sniffing
^ Laperdrix P, Rudametkin W, Baudry B (May 2016). Beauty and the Beast: Diverting Modern Web Browsers to Build Unique Browser Fingerprints. 2016 IEEE Symposium on Security and Privacy. San Jose CA USA: IEEE. pp. 878–894. doi:10. 1109/SP. 2016. 57. ISBN 978-1-5090-0824-7.
^ a b Eckersley P (2017). “How Unique Is Your Web Browser? “. In Atallah MJ, Hopper NJ (eds. ). Privacy Enhancing Technologies. Lecture Notes in Computer Science. Springer Berlin Heidelberg. pp. 1–18. ISBN 978-3-642-14527-8.
^ a b Cao, Yinzhi (2017-02-26). “(Cross-)Browser Fingerprinting via OS and Hardware Level Features” (PDF). Archived (PDF) from the original on 2017-03-07. Retrieved 2017-02-28.
^ Alaca F, van Oorschot PC (December 2016). Device Fingerprinting for Augmenting Web Authentication: Classification and Analysis of Methods. 32nd Annual Conference on Computer Security. Los Angeles CA USA: Association for Computing Machinery. pp. 289–301. 1145/2991079. 2991091. ISBN 978-1-4503-4771-6.
^ a b Steinberg J (23 July 2014). “You Are Being Tracked Online By A Sneaky New Technology — Here’s What You Need To Know”. Forbes. Retrieved 2020-01-30.
^ “User confidence takes a Net loss”. 2005-07-01. Archived from the original on 2015-10-04. Retrieved 2015-10-03.
^ “7 Leading Fraud Indicators: Cookies to Null Values”. 2016-03-10. Archived from the original on 2016-10-03. Retrieved 2016-07-05.
^ a b Nikiforakis N, Joosen W, Livshits B (May 2015). PriVaricator: Deceiving Fingerprinters with Little White Lies. WWW ’15: The 24th International Conference on World Wide Web. Florence Italy: International World Wide Web Conferences Steering Committee. pp. 820–830. 1145/2736277. 2741090. hdl:10044/1/74945. ISBN 978-1-4503-3469-3.
^ a b Acar G, Juarez M, Nikiforakis N, Diaz C, Gürses S, Piessens F, Preneel B (November 2013). FPDetective: Dusting the Web for Fingerprinters. 2013 ACM SIGSAC Conference on Computer & Communications Security. Berlin Germany: Association for Computing Machinery. pp. 1129–1140. 1145/2508859. 2516674. ISBN 978-1-4503-2477-9.
^ a b c Abgrall E, Le Traon Y, Monperrus M, Gombault S, Heiderich M, Ribault A (2012-11-20). “XSS-FP: Browser Fingerprinting using HTML Parser Quirks”. arXiv:1211. 4812 [].
^ a b c d e f Nikiforakis N, Kapravelos A, Wouter J, Kruegel C, Piessens F, Vigna G (May 2013). Cookieless Monster: Exploring the Ecosystem of Web-Based Device Fingerprinting. 2013 IEEE Symposium on Security and Privacy. Berkeley CA USA: IEEE. 2013. 43. ISBN 978-0-7695-4977-4.
^ “EFF’s Top 12 Ways to Protect Your Online Privacy | Electronic Frontier Foundation”. 2002-04-10. Archived from the original on 2010-02-04. Retrieved 2010-01-28.
^ “MSIE clientCaps “isComponentInstalled” and “getComponentVersion” registry information leakage”. Archived from the original on 2011-06-12. Retrieved 2010-01-28.
^ a b Kohno; Broido; Claffy. “Remote Physical Device Detection”. Archived from the original on 2010-01-10. Retrieved 2010-01-28.
^ “About Panopticlick”. Retrieved 2018-07-07.
^ Eckersley, Peter (17 May 2010). “How Unique Is Your Web Browser? ” (PDF). Electronic Frontier Foundation. Archived (PDF) from the original on 9 March 2016. Retrieved 13 Apr 2016.
^ a b Angwin J (July 21, 2014). “Meet the Online Tracking Device That is Virtually Impossible to Block”. ProPublica. Retrieved 2020-01-30.
^ a b c Mowery K, Shacham H (2012), Pixel Perfect: Fingerprinting Canvas in HTML5 (PDF), retrieved 2020-01-21
^ a b c Acar G, Eubank C, Englehardt S, Juarez M, Narayanan A, Diaz C (November 2014). The Web Never Forgets: Persistent Tracking Mechanisms in the Wild. 2014 ACM SIGSAC Conference on Computer & Communications Security. Scottsdale AZ USA: Association for Computing Machinery. pp. 674–689. 1145/2660267. 2660347. ISBN 978-1-4503-2957-6.
^ a b Davis W (July 21, 2014). “EFF Says Its Anti-Tracking Tool Blocks New Form Of Digital Fingerprinting”. MediaPost. Retrieved July 21, 2014.
^ Knibbs K (July 21, 2014). “What You Need to Know About the Sneakiest New Online Tracking Tool”. Gizmodo. Retrieved 2020-01-30.
^ “meta: tor uplift: sistFingerprinting”. Retrieved 2018-07-06.
^ “Firefox’s protection against fingerprinting”. Retrieved 2018-07-06.
^ “Firefox 42. 0 release notes”.
^ “Apple introduces macOS Mojave”. Retrieved 2018-07-06.
^ Gómez-Boix A, Laperdrix P, Baudry B (April 2018). Hiding in the Crowd: An Analysis of the Effectiveness of Browser Fingerprinting at Large Scale. WWW ’18: The Web Conference 2018. Geneva Switzerland: International World Wide Web Conferences Steering Committee. pp. 309–318. 1145/3178876. 3186097. ISBN 978-1-4503-5639-8.
^ “Firefox 69. 0 release notes”.
^ Vastel, Antoine; Laperdrix, Pierre; Rudametkin, Walter; Rouvoy, Romain (May 2018). FP-STALKER: Tracking Browser Fingerprint Evolutions. 2018 IEEE Symposium on Security and Privacy. 2018. 00008 – via Institute of Electrical and Electronics Engineers. CS1 maint: ref duplicates default (link)
^ “Chatter on the Wire: A look at DHCP traffic” (PDF). Archived (PDF) from the original on 2014-08-11. Retrieved 2010-01-28.
^ “Wireless Device Driver Fingerprinting” (PDF). Archived from the original (PDF) on 2009-05-12. Retrieved 2010-01-28.
^ “Chatter on the Wire: A look at excessive network traffic and what it can mean to network security” (PDF). Archived from the original (PDF) on 2014-08-28. Retrieved 2010-01-28.
^ Alexander, Sjosten; Daniel, Hedin; Andrei, Sabelfeld. “EssentialFP: Exposing the Essence of Browser Fingerprinting” (PDF). Retrieved 27 July 2021.
^ “User-Agent”.
^ Aaron Andersen. “History of the browser user-agent string”.
^ a b c Unger T, Mulazzani M, Frühwirt D, Huber M, Schrittwieser S, Weippl E (September 2013). SHPF: Enhancing HTTP(S) Session Security with Browser Fingerprinting. 2013 International Conference on Availability, Reliability and Security. Regensburg Germany: IEEE. pp. 255–261. 1109/ARES. 33. ISBN 978-0-7695-5008-4.
^ a b Fiore U, Castiglione A, De Santis A, Palmieri F (September 2014). Countering Browser Fingerprinting Techniques: Constructing a Fake Profile with Google Chrome. 17th International Conference on Network-Based Information Systems. Salerno Italy: IEEE. 1109/NBiS. 2014. 102. ISBN 978-1-4799-4224-4.
^ a b Takei N, Saito T, Takasu K, Yamada T (Nov 2015). Web Browser Fingerprinting Using Only Cascading Style Sheets. 10th International Conference on Broadband and Wireless Computing, Communication and Applications. Krakow Poland: IEEE. pp. 57–63. 1109/BWCCA. 2015. 105. ISBN 978-1-4673-8315-8.
^ Mulazzani M, Reschl P, Huber M, Leithner M, Schrittwieser S, Weippl E (2013), Fast and Reliable Browser Identification with JavaScript Engine Fingerprinting (PDF), SBA Research, retrieved 2020-01-21
^ Mowery K, Bogenreif D, Yilek S, Shacham H (2011), Fingerprinting Information in JavaScript Implementations (PDF), retrieved 2020-01-21
^ a b Upathilake R, Li Y, Matrawy A (July 2015). A classification of web browser fingerprinting techniques. 7th International Conference on New Technologies, Mobility and Security. Paris France: IEEE. 1109/NTMS. 7266460. ISBN 978-1-4799-8784-9.
^ Starov O, Nikiforakis N (May 2017). XHOUND: Quantifying the Fingerprintability of Browser Extensions. 2017 IEEE Symposium on Security and Privacy. pp. 941–956. 2017. 18. ISBN 978-1-5090-5533-3.
^ Sanchez-Rola I, Santos I, Balzarotti D (August 2017). Extension Breakdown: Security Analysis of Browsers Extension Resources Control Policies. 26th USENIX Security Symposium. Vancouver BC Canada: USENIX Association. pp. 679–694. ISBN 978-1-931971-40-9. Retrieved 2020-01-21.
^ a b Kaur N, Azam S, KannoorpattiK, Yeo KC, Shanmugam B (January 2017). Browser Fingerprinting as user tracking technology. 11th International Conference on Intelligent Systems and Control. Coimbatore India: IEEE. 1109/ISCO. 7855963. ISBN 978-1-5090-2717-0.
^ a b c d e f Al-Fannah NM, Li W (2017). “Not All Browsers are Created Equal: Comparing Web Browser Fingerprintability”. In Obana S, Chida K (eds. Advances in Information and Computer Security. Springer International Publishing. pp. 105–120. arXiv:1703. 05066. ISBN 978-3-319-64200-0.
^ a b Olejnik L, Castelluccia C, Janc A (July 2012). Why Johnny Can’t Browse in Peace: On the Uniqueness of Web Browsing History Patterns. 5th Workshop on Hot Topics in Privacy Enhancing Technologies. Vigo Spain: INRIA. Retrieved 2020-01-21.
^ “Privacy and the:visited selector – CSS: Cascading Style Sheets | MDN”.
^ Fifield D, Egelman S (2015). “Fingerprinting Web Users Through Font Metrics”. In Böhme R, Okamoto T (eds. Financial Cryptography and Data Security. 8975. pp. 107–124. 1007/978-3-662-47854-7_7. ISBN 978-3-662-47854-7.
^ Saito T, Yasuda K, Ishikawa T, Hosoi R, Takahashi K, Chen Y, Zalasiński M (July 2016). Estimating CPU Features by Browser Fingerprinting. 10th International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing. Fukuoka Japan: IEEE. pp. 587–592. 1109/IMIS. 108. ISBN 978-1-5090-0984-8.
^ Olejnik L, Acar G, Castelluccia C, Diaz C (2016). “The Leaking Battery”. In Garcia-Alfaro J, Navarro-Arribas G, Aldini A, Martinelli F, Suri N (eds. Data Privacy Management, and Security Assurance. DPM 2015, QASA 2015. 9481. Springer, Cham. 1007/978-3-319-29883-2_18. ISBN 978-3-319-29883-2.
^ Englehardt S, Arvind N (October 2016). Online Tracking: A 1-million-site Measurement and Analysis. Vienna Austria: Association for Computing Machinery. pp. 1388–1401. 1145/2976749. 2978313. ISBN 978-1-4503-4139-4.
^ Laperdrix, Pierre; Bielova, Nataliia; Baudry, Benoit; Avoine, Gildas (2020-04-19). “Browser Fingerprinting: A Survey”. ACM Transactions on the Web. 14 (2): 1–33. 1145/3386040. ISSN 1559-1131. S2CID 145051810.
^ Yen TF, Xie Y, Yu F, Yu R, Abadi M (February 2012). Host Fingerprinting and Tracking on the Web: Privacy and Security Implications (PDF). The 19th Annual Network and Distributed System Security Symposium. San Diego CA USA: Internet Society. Retrieved 2020-01-21.
^ Laperdrix, Pierre; Baudry, Benoit; Mishra, Vikas (2017). “FPRandom: Randomizing Core Browser Objects to Break Advanced Device Fingerprinting Techniques” (PDF). Engineering Secure Software and Systems. 10379: 97–114. 1007/978-3-319-62105-0_7. ISBN 978-3-319-62104-3.
^ “What’s Brave Done For My Privacy Lately? Episode #3: Fingerprint Randomization”. 6 March 2020.
^ Merzdovnik G, Huber M, Buhov D, Nikiforakis N, Neuner S, Schmiedecker M, Weippl E (April 2017). Block Me If You Can: A Large-Scale Study of Tracker-Blocking Tools (PDF). 2017 IEEE European Symposium on Security and Privacy. pp. 319–333. 1109/EuroSP. 26. ISBN 978-1-5090-5762-7.
^ Kirk J (July 25, 2014). “‘Canvas fingerprinting’ online tracking is sneaky but easy to halt”. PC World. Retrieved August 9, 2014.
^ Smith, Chris. “Adblock Plus: We can stop canvas fingerprinting, the ‘unstoppable’ new browser tracking technique”. BGR. PMC. Archived from the original on July 28, 2014.
^ Newman, Drew (2007). “The Limitations of Fingerprint Identifications”. Criminal Justice. 1 (36): 36–41.
Further reading[edit]
Fietkau, Julian (2020-12-28). “The Elephant In The Background: Empowering Users Against Browser Fingerprinting”. Chaos Communication Congress 2020.
Angwin, Julia; Valentino-DeVries, Jennifer (2010-11-30). “Race Is On to ‘Fingerprint’ Phones, PCs”. Wall Street Journal. ISSN 0099-9660. Retrieved 2018-07-10.
Segal, Ory; Fridman, Aharon; Shuster, Elad (2017-06-05). “Passive Fingerprinting of HTTP/2 Clients” (PDF). Akamai. Retrieved 2018-07-10.
Rybnik, Rafał (2020-10-04). “Fingerprinting”. Data Driven Investor.
External links[edit]
Panopticlick, by the Electronic Frontier Foundation, gathers some elements of a browser’s device fingerprint and estimates how identifiable it makes the user
Am I Unique, by INRIA and INSA Rennes, implements fingerprinting techniques including collecting information through WebGL.
Partial database of websites that have used canvas fingerprinting
What is browser fingerprinting? Here's how to prevent it

What is browser fingerprinting? Here’s how to prevent it

What is browser fingerprinting? It’s creepy, that’s what! It tracks your online behavior, allowing others to know who you are as you browse the internet — all without any need for account logins or cookies.
Just like a human fingerprint, your browser has a set of unique traits that can be traced back to you — and everything you do on the internet.
When you browse through the internet, many web portals capture some of this information, such as screen size and browser type, to give you the best experience.
However, browser fingerprints can also be used for tracking and identification. Websites can record all kinds of information about you through this fingerprint, and then connect it to other similar fingerprints to get a precise picture of your browsing behaviors and website activities.
According to PanoptiClick, there is a chance that one in 286, 777 browsers has the same browser fingerprint for another user. So the chances of overlapping browser are pretty slim. That’s why a browser fingerprint is an invaluable piece of information for marketers that want to sell you stuff!
Curious to see how unique your browser fingerprint is? Use one of the top browser fingerprinting checkers to see just how unique your fingerprint is! Then, you may want to try some of the tactics below to reduce your fingerprint.
How to prevent browser fingerprinting
If you are concerned about your personal information and don’t want to share it over the internet through browser fingerprints, you might want to stop it.
Unfortunately, there is only one method if you want to stop browser fingerprinting completely, which is not using the internet at all. Yes, it’s nearly impossible to keep your browsers from collecting your data because browsers use HTTP headers to collect your fingerprint.
There are certain security practices you can use to mitigate your browser fingerprint or make it unoriginal. The following practices will make your identity harder to track and prevent advertisers from learning extremely personal information about your web browsing history.
1. Disable Flash
If you are a Chrome user, then you should not worry about flash because Chrome will stop using it by the end of 2020. Moreover, many reputable sites have stopped using flash as well, so it’s a no-brainer because it’s becoming a relic of the past.
Most of the experts believe that flash serves no considerable purpose other than collecting fingerprint data. You can just disable it or uninstall it right away because you do not need it if you are not explicitly using it for a specific purpose.
Otherwise, it will keep tracking your data for a short time because the newer versions of all the major browsers already have decided to stop supporting it.
2. Revisit extensions and plugins
Browser extensions and plugins can be great assets for browsing. They can provide deeper integrations to the services you use every day. But these can also make it much easier for others to track you.
But think about it: the more extensions and plugins your browser has, the more unique your browser fingerprint. That configuration is much harder to replicate by someone else!
That’s why you should uninstall the plugins that you don’t use right away and try to use standalone desktop apps as alternatives to the ones you use.
Bear in mind that disabling the plugins doesn’t do any good. After all, it can still be used as your fingerprint because it stays in your browser. The most secure route is to use JavaScript disabling extensions. It disables JavaScript usage, unless you explicitly allow it. This will protect you from unwanted tracking!
3. Keep all of your software updated
In order to protect yourself from cybercriminals and hacking attacks, try to keep all of your software updated. It means you need to restart your browser and sometimes computer as well. It can be a little cumbersome, but it’s worth it if you want to reduce your browser fingerprinting.
The most important software that needs to be updated all the time is your anti-malware and anti-virus. Otherwise, such software won’t be able to detect the latest cyberattacks, exploits, and malware.
4. Use Incognito or private mode
Using an incognito mode of your browser is a wise idea to reduce fingerprinting. While it’s not perfect, it does reduce the amount of information shared with others. To see how it’s working, you can still visit any browser fingerprinting checker to see the results while you are in private or incognito mode that will most probably be unique.
For that matter, we recommend you use Tor for the most private browsing experience. If you have heard about Tor, you must’ve also heard that it’s for dark web browsing. Tor is most commonly used for that purpose, but it is also an excellent way to avoid all types of possible tracking.
5. Use Tor
The Tor browser is an extremely secure and private browser that includes anti-fingerprinting features, such as cloaking your operating system and blocking revealing information like your time zone and language preferences. Without these details it’s much harder for your browser to be fingerprinted.
A reminder though: the most anonymous way to use any Internet browser is to avoid installing extensions and plug-ins. Those are simply the easiest way to know who you are, since so few people have the same combination of installations. Stick with the default version to better anonymize your browser.
6. Use a VPN
A virtual private network boosts your online safety, security as well as privacy. It masks your address and physical location by routing your internet traffic through a third-party server. That way you appear like you’re browsing from someplace else.
VPNs can provide you protection against hackers, surveillance, ISPs (Internet Service Providers), and malicious, your data transmission is often encrypted so that no one can intercept it.
Keep in mind that VPNs don’t prevent websites from using JavaScript and HTTP headers to collect browser fingerprints. It removes your IP address from the headers and equation, but your fingerprint still might be unique. But you can always use the mixture of all the tips mentioned earlier along with the VPN to keep websites from collecting your fingerprinting data.
6. Ditch the smartphone
You’re really gonna hate this one…but giving up your smart phone is the quickest way to preserve your anonymity. Phones are basically mini-surveillance tools that also use device fingerprinting that make it easy to attach your identity to your online behaviors.
Next steps to protect your privacy online
Web trackers use many sneaky and technical ways to collect your browsing fingerprint. But we hope that these ways will help you reduce it as much as possible and allow your fingerprint not to appear unique on the internet. Feel free to let us know about your thoughts and expressions regarding browser fingerprints.
A few other steps to protect your privacy while browsing online:
Use a password management appDelete yourself from data brokersRethink free apps on your phoneMonitor the latest data breachesFind out what is device fingerprintingDelete social mediaRemove yourself from the internet

Frequently Asked Questions about browser finger

What is browser finger?

A browser fingerprint is information collected specifically by interaction with the web browser of the device.

How do you beat browser fingerprint?

How to prevent browser fingerprintingDisable Flash. If you are a Chrome user, then you should not worry about flash because Chrome will stop using it by the end of 2020. … Revisit extensions and plugins. … Keep all of your software updated. … Use Incognito or private mode. … Use Tor. … Use a VPN. … Ditch the smartphone.Oct 14, 2020

What does browser fingerprint reveal?

With browser fingerprinting, a lot of information can be gathered from the browser: the user’s device model, its operating system, its browser version, browser extensions, user timezone, preferred language settings, ad blocker used, screen size and resolution, and all the granular tech specs of his CPU, graphics card, …Apr 1, 2021

Leave a Reply

Your email address will not be published. Required fields are marked *