Web-based Fingerprinting Techniques
ıtor Bernardo and Dulce Domingos
LaSIGE, Faculdade de Ci
encias, Universidade de Lisboa, Lisboa, Portugal
Browser Fingerprinting, Cross-browser Fingerprinting, Device Fingerprinting, Privacy, Fingerprint.
The concept of device fingerprinting is based in the assumption that each electronic device holds a unique set
of physical and/or logical features that others can capture and use to differentiate it from the whole. Web-based
fingerprinting, a particular case of device fingerprinting, allows website owners to differentiate devices based
on the set of information that browsers transmit. Depending on the techniques being used, a website can track
a device based on its browser features (browser fingerprinting) or based on system settings (cross-browser
fingerprinting). The latter allows identification of the device even when more than one browser is used.
Several different works have introduced new techniques over the last years proving that fingerprinting can be
done in multiple ways, but there is not a consolidated work gathering all of them. The current work identifies
known web-based fingerprinting techniques, categorizing them as which ones are browser and which are
cross-browser and showing real examples of the data that can be captured with each technique. The study is
synthesized in a taxonomy, which provides a clear separation between techniques, making it easier to identify
the threats to security and privacy inherent to each one.
Device fingerprinting is based on the assumption that
no two devices are exactly alike and that profiles can
be created by capturing the emanation patterns sent
or leaked from the devices, as long as these extern-
are repetitive through time. Small phys-
ical differences in the components of the devices, that
were introduced during the manufacturing process,
may result in slightly different behaviours and extern-
alizations, which could be captured and used to create
a fingerprint (Jenkins et al., 2014). Other examples of
fingerprinting could be the tracking of clock deviation
between the internal clock of a client’s device and the
clock of a server (Kohno et al., 2005) or the collection
of the set of fonts and browser plugins installed on a
system (Eckersley, 2010).
Web-based fingerprinting, a particular case of
device fingerprinting, allows a website owner to track
devices’ accesses throughout time, in an almost invis-
ible way. In fact, even if the user is aware of privacy
issues and takes precautions, whether by actively de-
leting cookies, blocking all cookies or using a browser
in “private mode”, his device will still be fingerprint-
able. This makes the use of web-based fingerprinting
The expression “externalization” refers to any display of
activity emanating from a certain device.
far more upsetting than simple cookies.
In most cases, web-based fingerprinting is used to
track users activity in sites and bind a device finger-
print to a user profile (together with its preferences,
tastes and interests). The interest of advertising com-
panies in this kind of information is foreseeable, as
it allows them to adjust the publicity to the users in-
Indeed, in 2014, the Article 29 Data Protection
Working Party, a European Union advisor on data pro-
tection, stated that: (...) fingerprint provides the
ability to distinguish one device from another and can
be used as a covert alternative for cookies to track in-
ternet behaviour over time. As a result, an individual
may be associated, and therefore identified, or made
identifiable, by that device fingerprint. (...) The data
protection risks of device fingerprinting are increased
by the fact that the unique set of information elements
is not only available to the website publisher, but also
to many other third parties. (Article 29 Data Protec-
tion Working Party, 2014) (p. 6).
Web-based fingerprinting relies in capability to
collect information about the device’s operating sys-
tem (OS) properties, installed software, and other lo-
gical configurations to get a unique signature from the
device - rather than trying to infer patterns from the
behaviour of the equipment, such as the “hardware-
Bernardo, V. and Domingos, D.
Web-based Fingerprinting Techniques.
DOI: 10.5220/0005965602710282
In Proceedings of the 13th International Joint Conference on e-Business and Telecommunications (ICETE 2016) - Volume 4: SECRYPT, pages 271-282
ISBN: 978-989-758-196-0
2016 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
based” device fingerprinting approach would. Web-
based fingerprinting does not require special equip-
ment or a specific scenario to be put into practise.
Two different approaches can be taken regarding
the type of client-side features that will be processed
to extract a signature: browser or cross-browser fin-
gerprinting. Browser fingerprinting relies on the dis-
tinctive features of the client browser and/or OS and
its purpose is to create a unique signature based on
the information collected from the pair (browser, OS).
Cross-browser fingerprinting, on the other hand, is
based only in non-browser features, what makes it re-
silient to the use of multiple browsers by the same
device. This approach requires system settings to be
collected such as, OS version, CPU information, net-
work interfaces information, number of processors,
screen size, etc.
The next section describes how web-based finger-
printing works. Section 3 depicts the most promin-
ent works addressing web-based fingerprinting tech-
niques. The bulk of the document is focused in the
analysis of the techniques (section 4), together with
the results of the tests using different browsers. The
taxonomy of web-based fingerprinting techniques can
be found at the end of this section. Finally, section 5
concludes the paper, discussing the threats to security
and privacy inherent to web-based fingerprinting and
possible mitigations.
In a typical web-based fingerprinting scenario, the cli-
ent’s browser requests the webpage code to the server
(through an HTTP request), in order to render it for
the user. Within this request, some fingerprinting in-
formation from the user’s system can already be col-
lected, namely the information that is sent in the User-
Agent - the HTTP header field that the most popular
browsers use to indicate the browser and OS versions.
In the HTTP response, the web server can include de
client-side fingerprinting script. Most fingerprinting
techniques are based in client-side code execution be-
cause this type of technology allows the browser to
make direct calls to the OS or other machine config-
urations and to send that data back to the webserver
asynchronously (without interfering with the display
and behaviour of the existing page).
After the browser has processed the script and
sent the result to the web server, the later computes
a unique identifier based on the information received
and stores it in a database - this will be the fingerprint
for this user’s device. Although this identifier could
be seen as a simple hash of the device’s properties,
this would soon prove to be a simplistic approach as
it would render the fingerprint useless at the slightest
change in the client’s environment. A better approach
would be to store an array of the device’s properties.
From this moment on, the website owner can track
that device throughout the pages it visits, without the
use of cookies, as long as they all contain the finger-
printing script.
It is important to note that the tracking can be done
among different sites or domains, as long as all the
websites share the same fingerprinting database.
In a different model of fingerprinting, the agent
doing the tracking might not be the website provider
but a third-party agent. This scenario is described
in (Nikiforakis et al., 2013). A site owner can agree
with an advertisement company the deployment of a
content from the later in the webpage, which visitors
will retrieve unknowingly, containing a fingerprint-
ing script. This version presents a more disturbing
scenario: the fingerprinting agent can track users on
multiple third-party websites, as long as the site own-
ers agree to include the advertisement. This allows a
more intrusive profiling of the web users as different
kinds of websites (with different subjects) provide a
richer view of the individual.
As far as we are aware, (Mayer, 2009) was the first au-
thor to present a fingerprint based on browser plugins
and mime-types. (Eckersley, 2010) also discusses
these techniques and introduces other cross-browser
approaches, such as font detection and OS features.
This work paved the way for web-based fingerprinting
and was the basis for the creation of Electronic Fron-
tier Foundation’s tracking awareness website, Panop-
Fingerprinting based on HTTP Header Fields is
one of the most explored techniques and (Eckersley,
2010), as well as (Mowery et al., 2011), (Nikifora-
kis et al., 2013), and (Acar et al., 2013) discuss this
The study that (Nikiforakis et al., 2013) present is
the one that shares most resemblance with the present
work. It describes some of the practices of device
identification through web-based fingerprinting tech-
niques available by then and measures the adoption of
fingerprinting on the web. The authors also present a
taxonomy for the fingerprinting techniques found in
“three large, commercial companies” along with Pan-
Available at https://panopticlick.eff.org
SECRYPT 2016 - International Conference on Security and Cryptography
opticlick. Although very enlightening, the study is
limited to the techniques that the three software man-
ufacturers and the Panopticlick website used at the
time. Therefore, other techniques, such as HTML5
canvas fingerprinting or exploitation of DNS leaks,
are not covered.
(Khademi et al., 2015) show the effectiveness of
some of the techniques depicted in the current work,
by implementing a web-based hybrid fingerprinting
tool: Fybrid.
Focusing on mitigation techniques, (Nikiforakis
et al., 2015) propose a tailored browser (PriVaricator)
which makes every visit appear different to a finger-
printing site, resulting in a different fingerprint for
each visit. We also discuss mitigation techniques in
section 5.
This section describes our study on web-based finger-
printing techniques. The analysed techniques were
gathered from multiple sources, ranging from pub-
lished papers to websites used with a fingerprinting
purpose - mostly, websites that show how web-based
fingerprinting is possible. These sources are refer-
enced along this section.
In our tests, we used different browsers: Moz-
illa Firefox 38.0.1; Microsoft Internet Explorer
10.0.9200.17357 (MS IE); Google Chrome ver.
43.0.2357.81 m; Opera/9.80 (Windows NT 6.2;
WOW64) Presto/2.12.388 Version/12.17; QtWeb In-
ternet Browser 3.8.5 (build 108); Midori 0.5.10 and
Chromium Portable version 44.0.2383.0.
After analysing the techniques, our results in a
taxonomy are synthesized in a taxonomy.
4.1 IP Address
The use of network mechanisms that changes the
user’s external IP address (e.g. NAT systems or
proxies) makes this information unreliable for track-
ing purposes. Still, while not providing enough
information to create a fingerprint, the IP address
can help adjusting and completing other methods of
fingerprinting. IP addresses are sent in the Internet
Layer of the TCP/IP model so, in theory, the website
always receives this information. The collection of
the IP addresses provides information to perform
cross-browser fingerprinting.
Advantages: The IP address of the originator is sent
for every HTTP request without requiring any special
scripting from the website owner.
Disadvantages: With the widespread use of NAT, the
tracking of a device through its IP address became
unfeasible. Moreover, the ever-growing use of TOR
networks and IP spoofing methods demands caution
when associating an IP address to a device.
This technique has two variations that are de-
scribed next.
a) Local IP Address (IPv4): The website
allows checking if the client browser
uses the WebRTC API. WebRTC is a browser-to-
browser application for voice calling, video chat,
and P2P file sharing. WebRTC implements STUN
(Session Traversal Utilities for Nat)
, a network
protocol that allows an end host to discover the public
IP address even when it is located behind a NAT.
With information about the client’s local IP address,
it is possible to verify if two different signatures
with the same external IP address correspond to two
different devices behind a NAT.
Advantages: Allows to distinguish different devices
behind a NAT.
Disadvantages: Some browsers lack the native
support of WebRTC, what makes them resilient to
this technique. At the time of writing, Microsoft’s
Internet Explorer was one of them.
b) DNS Leaks: When a DNS request is made to
the default DNS servers (usually belonging to the
internet service provider), instead of the anonymous
DNS servers assigned by the anonymity network, it
is considered a “DNS leak”. In this technique, the
website generates a certain number of non-existent
second level domain names and includes them in the
code of the requested page. When the client browser
finds the references to these domains in the page
code it tries to resolve them, by querying its DNS
servers. The DNS servers, in turn, will not have any
cached registry for those domains (because they do
not exist), so they will request for addresses to the
domain authority. Once the DNS servers detect the
call to the fake domains they forward the requests to
the fingerprinter, together with the names of the DNS
servers requesting them. Then, the fingerprinter can
correlate the faux domains with the IP address that
requested the page, and associate an array of DNS
servers to that profile.
Advantages: The data collection does not rely on any
scripting, making it more effective.
Available at https://ipleak.net
See http://www.voip-info.org/wiki/view/STUN
Web-based Fingerprinting Techniques
Disadvantages: Requires access to the domain au-
thority DNS servers.
4.2 HTTP Request Header Fields
The HTTP protocol specification describes a series of
request header fields (Fielding and Reschke, 2014).
Because HTTP header fields are a well-known source
of information, many authors have mentioned them
in their works ((Eckersley, 2010), (Nikiforakis et al.,
2013), (Acar et al., 2013), (Mowery et al., 2011)).
Next, we describe four of the HTTP Header fields
that are used to gather information about the client’s
system. Advantages and disadvantages are presented
in the end of the subsection, as all of them share the
same features.
a) Accept Field: This field allows the browser to
inform the server about the Content-Types that are
acceptable for the response - the media types that
the browser understands and how well it understands
them. This field provides browser related informa-
tion and can contribute to the browser fingerprinting.
Table 1 shows the values sent by 3 different browsers.
Table 1: Three different Accept field values.
Browser Accept value
text/html, application/xhtml+xml, applica-
text/html, application/xhtml+xml, applica-
tion/xml;q=0.9, image/webp, */*;q=0.8
MS IE text/html, application/xhtml+xml, */*
b) Accept-Encoding Field: Compliant browsers
should announce to the server what methods they sup-
port before downloading the correct format. This field
provides browser related information and can contrib-
ute to the browser fingerprinting. Table 2 shows 3
possible values for this field.
Table 2: Three different Accept-Encoding field values.
Browser Accept-Encoding value
Mozilla Firefox gzip, deflate
Google Chrome gzip, deflate, sdch
QtWeb gzip
c) Accept-Language Field: A webpage might have
the same content available in several languages. The
language can be selected automatically by the server,
based on the preference indicated in this field. This
property also contributes to browser fingerprinting.
Table 3 shows 3 different values for the Accept-
Language field.
Table 3: Three different Accept-Language field values.
Browser Accept-Language value
QtWeb pt-PT,en,*
Google Chrome en-US,en;q=0.8
MS IE pt-PT,pt;q=0.8,en-GB;q=0.5,en;q=0.
d) User-Agent Field: This field can provide specific
information about the browser brand and version. It
also allows inferring the type of OS, which makes it
useful for browser fingerprinting and cross-browser
fingerprinting at the same time. Table 4 shows 3 dif-
ferent possible values for this field.
Table 4: Three different User-Agent field values.
Browser User-Agent value
Mozilla/5.0 (Windows NT 6.2; WOW64;
rv:38.0) Gecko/20100101 Firefox/38.0
MS IE Mozilla/5.0 (compatible; MSIE 10.0; Win-
dows NT 6.2; WOW64; Trident/6.0)
Mozilla/5.0 (Windows NT 6.2; WOW64)
AppleWebKit/537.36 (KHTML, like Gecko)
Chrome/43.0.2357.81 Safari/537.36
Advantages: The biggest advantage of using HTTP
headers is that every incoming request contains a set
of them, making it easier for the webserver to col-
lect the information. Therefore, no client-scripting is
Disadvantages: The HTTP fields do not provide
enough diversity to create a unique signature. This
technique must always be used complementary to oth-
ers. Moreover, a user with a customized browser
could clean or modify the header files, affecting the
4.3 Browser Properties
System settings such as the type of contents sup-
ported by the browser, the local clock time or the
client’s system default language can be externalized
by the browser software. We discuss advantages and
disadvantages in the end of this subsection, as they
are common for both techniques.
a) Plugin and Mime-type Enumeration: When a
website needs to query if a certain plugin exists in the
clients system, to properly display/run some type of
content, it can retrieve an array of plugins by access-
ing to JavaScript’s navigator.plugins. This tech-
nique is described in the following works: (Eckersley,
2010), (Nikiforakis et al., 2015), (Acar et al., 2013)
and (Mayer, 2009).
This information can be used for both browser fin-
gerprinting and cross-browser fingerprinting.
SECRYPT 2016 - International Conference on Security and Cryptography
An example of the information we can get with
this technique is: Plugin 0: Adobe Acrobat; Plugin 2:
Google Update; Plugin 3: Java Deployment Toolkit
b) Do-Not-Track: The Do-Not-Track header was a
proposed HTTP header field (DNT) with the object-
ive of increasing the privacy of the webpage users.
The header contains a flag that indicates whether the
client is willing to be tracked across websites. Unfor-
tunately, the browser user has no control over whether
the request is honoured or not, so the effectiveness of
this measure is arguable.
The Do-Not-Track header can be retrieved us-
ing the JavaScript language and calling the value
(Mayer and Mitchell, 2012), (Acar et al., 2013)
and (Nikiforakis et al., 2013) mentioned the Do-Not-
Track header and agreed that its usefulness is ques-
tionable, at best. Because it produces slightly differ-
ent answers across browsers (see Table 5 below), this
technique can add diversity to the browser fingerprint-
Table 5: Three different Do-Not-Track field values.
Browser Do-Not-Track
Mozilla Firefox unspecified
MS IE undefined
Google Chrome null
Advantages: The retrieval of the browser’s properties
provides an extensive list of browser related informa-
tion, including plugin versions, which adds diversity
to the profiling process.
Disadvantages: The browser plugins are pieces of
software that can be removed or updated (to new ver-
sions). The creation of a signature should not rely
on a simple hash of the plugin list, or else the fin-
gerprint would be rendered useless right after the first
plugin update. Also, browsers react differently to the
navigator.plugins request.
4.4 Browser Behaviour
Browser vendors are free to include their own logic
in the code that is embodied in their software. This
means that, the outcome of a certain operation
when performed by two different browsers might
be the same, although their underlying mechanisms
are different. In this section we describe different
browser behaviour techniques.
a) Browser Rounding and Fractional Pixels: The
way that different browser handle math calculations,
and rounding in particular, can be used to identify the
client’s browser and contribute to the fingerprinting
The webpage “Browser Rounding and Fractional
contains a test page where users can test
their browser’s behaviour. The site calculates per-
centages of decimal values for multiple graphic boxes
with fractional pixels. Being a browser benchmark-
ing technique, this approach can contribute to per-
form browser fingerprinting. The different results for
browser rounding and fractional pixels of an element
(Box2) are shown in Table 6.
Table 6: Three different results for browser rounding and
fractional pixels.
Browser Calculated width for Box2
Mozilla Firefox 666.8499755859375px
MS IE 667px
Google Chrome 666.859375px
Advantages: Unlike other browser behaviour-related
approaches that are affected by the system usage
at a given time (e.g. CPU load or allocated RAM
memory), this technique is unaffected by such
Disadvantages: This approach is not enough to
create a unique signature. At most, it can help
identifying the browser, but it requires a comparison
database to relate the measured patterns with the
browsers’ brands and models.
b) Canvas Fingerprinting: The canvas fingerprint-
ing technique applies a principle similar to the previ-
ously shown: the rendering of a graphical object by
different systems (browsers) produces different out-
put, and therefore different fingerprints. (Mowery
and Shacham, 2012) show that the rendering of fonts
and graphical elements have slight variations between
different browsers, what allows the extraction of an
individual signature.
In their study about canvas fingerprinting and
evercookie feasibility, (Acar et al., 2014) crawled the
Top Alexa 100,000 sites and found that “more than
5.5% of crawled sites actively ran canvas fingerprint-
ing scripts on their home pages.
The website BrowserLeaks.com
provides a page
to test canvas fingerprinting, but warns that the com-
parison database, used for matching the incoming sig-
natures, is not complete enough to provide a complete
coverage of the whole universe of browsers and does
not collect new signatures.
Available at http://cruft.io/posts/percentage-calculations-
Available at https://www.browserleaks.com/
Web-based Fingerprinting Techniques
This technique can contribute to perform browser
fingerprinting. The results of a test with 3 different
browsers can be seen in Table 7.
Table 7: Three different browser signatures built from can-
vas fingerprinting.
Browser Signature Website general conclusion
5525E5D4 “It is very likely that you are us-
ing [Firefox] on [Windows]”
MS IE 62939B59 “It is very likely that you are us-
ing [Internet Explorer] on [Win-
F921F32B “Your system fingerprint appears
to be unique (...)”
Advantages: Similarly to “Browser rounding and
fractional pixels”, this technique is not affected by
the computing workload at the moment of the data
Disadvantages: The technique is only reliable when
backed up by a database that maps the whole browser
signature universe and that is constantly collecting
new signatures in order to be updated.
c) Browser Performances: (Mowery et al., 2011)
presents a technique for measuring timing differences
of multiple operations in the core of the JavaScript
language, which can contribute to perform browser
fingerprinting. According to the authors, it would
be possible to distinguish not only browser versions
but also micro architectural features not normally ex-
posed to JavaScript.
The results retrieved from the website V8 Bench-
mark Suite
can be seen in Table 8.
Table 8: Example of JavaScript Performance Benchmarking
for the Mozilla Firefox browser, retrieved from the website
V8 Benchmark Suite.
Richards 5790 6023 6051 5871
DeltaBlue 10731 20556 13052 1950
Crypto 4957 4556 7585 4404
Advantages: Does not rely on information that is
communicated by the browser. It rather observes the
behaviour and registers the results. This makes the job
harder for those trying to develop anti-fingerprinting
browsers, because the simple mitigation of the data
sent is not enough to protect against this technique.
Disadvantages: The performance information col-
lected from the user’s system is highly dependable on
Available at http://v8.googlecode.com/svn/data/ bench-
the processing being done at that moment and, there-
fore, different tests might show large discrepancies in
the time values. On other hand, the authors of this
work agree that [o]ne of the largest weaknesses in
[the] approach is that the fingerprinting time is very
large - usually over 3 minutes (p. 3). In fact, while
the tests are being performed, the whole client system
is affected by a sudden degradation of performance,
mostly noticed by the browser’s lack of responsivity.
This undermines any possibility of using this tech-
nique on a stealthy way.
4.5 Operating System Features
System related settings, such as the OS version, the
amount of available RAM or the current system time
are features that can also be retrieved to create cross-
browser fingerprints.
Java and Flash plugins are known to access
system settings in a fashion that is not totally privacy-
friendly, as they bypass the browser’s controls. The
TorProject team (developers of the Tor Browser),
for instance, warns their users about the threats to
anonymity associated with the use of Java and Flash
. The collection of OS related information
using these technologies is mentioned by (Eckersley,
2010) and (Nikiforakis et al., 2013), although not
extensively debated. The universe of information
possible to retrieve from the system with the Java
technology is significantly greater than the one
provided by Flash.
a) Using Flash Plugin: The online ActionScript 3.0
Reference for Adobe Flash Platform website
that “[t]he Capabilities class provides properties that
describe the system and runtime that are hosting the
application. (...) By using the Capabilities class to
determine what capabilities the client has, you can
provide appropriate content to as many users as pos-
The data collection through the Flash plugin al-
lows browser fingerprinting and cross-browser finger-
printing, because there is plugin-related information
(browser configuration) and system information be-
ing retrieved. Table 9 shows a subset of the results
retrieved from the site BrowserLeaks.com
b) Using Java: The Java plugin can provide informa-
tion about the local Java Virtual Machine (JVM). This
technique provides system-related information, there-
fore allowing to perform cross-browser fingerprint-
Available at https://www.torproject.org/docs/faq.html.en
Available at http://help.adobe.com/en US/FlashPlatform/
Available at http://https://www.browserleaks.com
SECRYPT 2016 - International Conference on Security and Cryptography
Table 9: Information retrieved from the website Browser-
Leaks.com for the Mozilla Firefox and Google Chrome.
Plugin info Mozilla Firefox Google Chrome
Shockwave Flash
17.0 r0
Shockwave Flash
18.0 r0
17 0 0 188.dll
ing. Table 10 shows a subset of the Java plugin data
retrieved from the website BrowserLeaks.com, for the
browsers Internet Explorer and Midori.
Table 10: Information retrieved from the website Browser-
Leaks.com for the Internet Explorer and Midori browsers.
JVM info MS IE Midori
JVM Uptime 1999278 1871881
JVM Start Time 1435274207429 1435274336050
Compilation Time 334 285
Advantages: This technique provides a fair amount
of system-related information that is collected by the
plugins, via API calls to the OS. Java plugin returns
the information it reads from the JVM which, by
default, is common to all Java-dependable software
on a particular system.
Disadvantages: The approach using Java is the
one that provides the most verbose information
(i.e. actual system data, instead of Flash’s Boolean
properties), making it more suitable for creating
a system signature. However, because nowadays
most of the browsers are currently prompting users
about whether they are sure to run Java Applets, it
is difficult to collect this information in a stealthy
c) Clock Skew: (Kohno et al., 2005) suggested
the fingerprinting of devices through comparison of
the clock skew between the client system and the
server clock. According to the authors, it would
be possible to exploit deviations in clock skews
“even when the measurer is thousands of miles,
multiple hops, and tens of milliseconds away from
the fingerprinted device, and when the fingerprinted
device is connected to the Internet from different
locations” (p. 1). The authors stressed that “one
can use [the] TCP timestamps-based method even
when the fingerprintee’s system time is maintained
via NTP”. This technique allows to perform cross-
browser fingerprinting.
Advantages: This technique allows the identification
of different devices behind a NAT and even the detec-
tion of virtual honeynets (the performed test showed
that virtual machines did not have constant, or near
constant, clock skews).
Disadvantages: The measuring duration of the tests
showed in the work of (Kohno et al., 2005) range
up to 120 minutes which is a rather high expectancy
time for a user to be browsing on a certain domain.
Moreover, the authors show the limitations of this
technique when they state that the technique does “not
provide unique serial numbers for devices, but (...)
skew estimates”.
4.6 Hardware Features
Browsers can access hardware settings, such as the
screen resolution, number of CPU cores, the amount
of dedicated memory or the identification of network
interfaces. Although the combination of the above
settings is far from being able to provide a unique
set, once combined with browser settings and other
fingerprinting methods they can contribute to the
creation of a unique signature.
a) Screen Properties: The retrieval of the screen
properties is possible to achieve through JavaScript or
Flash. The use of JavaScript’s window.screen ob-
ject allows the retrieval of basic information about
the screen of the client. It is possible to gather in-
formation about screen height (in pixels), width, and
bit depth of the colour palette available for displaying
images. This type of data collection was addressed
in (Acar et al., 2013) with the use of a fingerprinting
detector, the FPDetective.
An example of screen properties can be seen in
Table 11, taken from the website BrowserLeaks.com.
Table 11: Screen properties for three different browsers,
taken from the website BrowserLeaks.com.
Property Mozilla Firefox Midori QtWeb
availHeight 860 900 860
colorDepth 24 24 32
pixelDepth 24 24 32
Advantages: The collection of screen size values
allows the fingerprinter to infer if the client’s device
is a smartphone, a tablet or a two-screen desktop
computer (the widths of both screens would be
Disadvantages: As shown in the example, cau-
tion must be taken when processing these values.
Different browser implementations can change
the measuring rules, therefore producing different
signatures and jeopardizing any cross-browser finger-
printing attempt.
b) CPU and RAM Memory: The Java technology
Web-based Fingerprinting Techniques
allows to retrieve information about the number of
available processors, and the amount of free, max and
total RAM memory. Browsers showed different beha-
viour for this data retrieval: while Internet Explorer,
Mozilla Firefox and Midori provided the requested in-
formation, others returned no data at all. This tech-
nique contributes to cross-browser fingerprinting, as
Java accesses OS interfaces.
Table 12 shows the different values for CPU and
RAM settings retrieved using a Java plugin with the
Internet Explorer and the Mozilla Firefox browsers.
Table 12: Examples of different results for (same) CPU and
RAM settings.
CPU and
Free Memory 8.135.728 10.363.232
Max Memory 259.522.560 259.522.560
Total Memory 16.252.928 16.384.000
Advantages: This technique provides very specific
system-related information that might help charac-
terize the type of web user (e.g. a tuned up system
might indicate a gamer or a graphics designer), which
might be interesting information for website owners
or advertisers.
Disadvantages: Properties like “Free memory” or
“Total memory” should not be relied upon, as they
are not consistent.
c) Network Interfaces Enumeration: Java enables
the retrieval of the name and status of physical and
virtual networks. Among the type of information re-
turned is the name of the interface, the status (i.e. if
the interface is enabled or not), the Maximum Trans-
mission Unit (MTU), if it is point-to-point, if it sup-
ports multicast, if it is loopback, and if the interface is
The information retrieved with this technique
is system-related, therefore it is suitable for cross-
browser fingerprinting. Table 13 presents one ex-
ample of the 106 network interfaces retrieved from
Table 13: One of the 106 network interfaces retrieved from
the website BrowserLeaks.com for the Internet Explorer
VMware Virtual Ethernet Adapter for VMnet8
Interface Is Up MTU Point-To-Point Multicast
eth41 true 1500 false true
Advantages: This technique is able to quickly
identify if the device being used at the current time
is the same used before. This feature can have prac-
tical application in situations where a website wants
to make sure that the user that signs in using a differ-
ent hardware from the usual is, in fact, the real user.
In case of an incoming new signature, the website can
prompt the user with a security question or request a
multi-channel authentication.
Disadvantages: The hardware’s collectable informa-
tion is slim and there is not enough diversity in order
to build a unique signature. Therefore, fingerprinting
by collection of hardware features must rely on addi-
tional techniques in order to be effective.
4.7 Font Detection/Enumeration
Different OSs have their own font sets. On top of that,
software suites such as Microsoft Office or Adobe
Creative Suite usually add their own set of fonts to the
system. Additionally, users can add their own fonts to
the system, as well. The resulting set of fonts can be
diverse enough to build a signature or, at least, allow
the identification of the OS. (Boda et al., 2012) show
how the universe of fonts is affected by the installation
of Office and Adobe suites. Fingerprinting by font
enumeration is mentioned in the works of (Eckers-
ley, 2010), (Nikiforakis et al., 2013), and (Acar et al.,
2013), making this one of the most commonly men-
tioned fingerprinting techniques, together with plugin
detection. Fonts can be enumerated using JavaScript,
Flash, Silverlight or Java, with different levels of effi-
Because system fonts are managed at the OS
level, this technique allows to perform cross-browser
fingerprinting. As stated in the website latit.lab
, the
font detection done through JavaScript and CSS takes
advantage of the fact that “each character appears
differently in different fonts. So different fonts will
take different width and height for the same string
of characters of same font-size”. By measuring the
output, the fingerprinter is able to identify if the
rendered font is different from the fall back and, if
that case, resident in the system. Table 14 shows 3
different results for different browsers.
Table 14: Font detection performed by the website lalit.org
for three different browsers.
Browser JavaScript and CSS Flash
MS IE 276 of 512 font 276 fonts
Google Chrome 231 of 512 fonts 270 fonts
Opera 264 of 512 fonts 276 fonts
Advantages: The enumeration of fonts is, possibly,
the data collection technique that provides the biggest
Available at http://www.lalit.org/lab/javascript-css-font-
SECRYPT 2016 - International Conference on Security and Cryptography
amount of data from the client’s system. Although
the list of font sets can change (e.g. fonts might be
actively added by the user or added because new soft-
ware was installed), the fingerprinter can still find this
risk negligible and decide to create a signature from
the list of fonts. OSs can also be inferred from the
font sets, as long as the fingerprinter has a database
of specific OS featuring fonts. The same applies for
Disadvantages: In order to perform the fingerprint-
ing using JavaScript, a database of fonts is needed for
comparison. A complete identification of the fonts
depends on having the most extensive database of
fonts possible, to encompass all fonts that might pos-
sibly appear.
4.8 Cached Objects
The technique of using stored objects on the client
side as a way to uniquely identify a device is fairly
similar across the technologies that have this ability:
first the fingerprinter checks the cached object holder
for the presence of any previous unique ID (stored
from a former visit). If no ID exists the website uses
the cached object to store a somewhat random but
unique ID that will allow further visits to be tracked
and linked to this device. Flash and HTML5 have
their own mechanisms for storing data, which are de-
scribed furthermore.
Strictly speaking, the exploitation of cached
content cannot be considered a fingerprinting tech-
nique. After all, devices are not being identified by
a set of unique features but rather, by a distinctive
mark that has been previously etched in each one.
However, for the unique identifier to be stored in the
system in the first place, it was necessary to perform
a previous verification of “non-existence” of that
device in the collected universe, which is, by itself, a
fingerprinting-like processing.
a) HTTP Cache: The caching of webpage contents
allows to reduce bandwidth usage, waiting times
and server load. When requesting a resource it
has downloaded and stored previously, the client
browser indicates the version of the stored object and
asks the server if it should download a newer one.
An exploitation for this behaviour was described
in (Zalewski, 2012): if the server has the chance
to indicate the client’s browser that it should or not
replace a file already stored on its system, then it has
a certain degree of control over that file and can use
it as an ID for tracking purposes.
Advantages: Most browsers allow the storing of
objects by default, especially when used in mobile
devices, making this technique reliable for finger-
printers. Systems do this to reduce bandwidth usage
and to increase access speed, as stated before.
Disadvantages: Devices configured not to store
cache or to delete cached objects when the browser
session is over are not affected by this technique,
although those that allow the storing during session
could be tracked throughout that period.
b) Browsing History: The collection of the URLs
in the browser’s history is done by using the tech-
nique described in (Janc and Olejnik, 2010), which
allows “an attacker to determine if a particular URL
has been visited by a client’s browser through apply-
ing CSS styles distinguishing between visited and un-
visited links. (...) the attacker must supply the client
with a list of URLs to check and infer which links ex-
ist in the client’s history by examining the computed
CSS values on the client-side” (p. 4).
Because the creation of a signature of the brows-
ing history would be rather useless, the authors de-
cided to create “history profiles” (that act as finger-
prints), where URLs are categorized by website sub-
ject (e.g. Shopping, Entertainment, Travel, Vehicles,
etc.). In (Olejnik et al., 2012), the authors state that “a
large number of users have unique personal browsing
interests even when analysed using the more coarse-
grained category metric (...) In a real scenario of an
advertising provider, multiple repetitions of each cat-
egory in the profiles are likely used to enumerate the
strength of interest in the category which provides ad-
ditional information” (p. 9).
This test was performed in a universe of 368.284
web histories, and more than 69% of users had a
unique fingerprint. This technique can contribute to
browser fingerprinting.
Advantages: This technique is a very appealing ap-
proach for advertisers, as they get more information
with less processing on their side (i.e. if browsing
profiles are already categorized into website subjects,
they do not have to associate signatures with hits).
Disadvantages: (Olejnik et al., 2012) ”believe
that Web browsing preferences can be used as an
efficient behavioural fingerprint which is in many
cases stable over time” (p. 14). The question is how
long can it take to have a stable browsing fingerprint?
The technique assumes that the user accesses the
website frequently, creating temporary profiles but,
for sporadic users this approach is not reliable. On
other hand, if a device is shared by multiple users
using the same account, the technique might not be
able to converge the patterns into a single profile, due
to the different browsing behaviour of each user.
Web-based Fingerprinting Techniques
c) Local Shared Objects (Flash Cookies): Using
previous versions of Flash, developers could save in-
formation between sessions by using “normal” cook-
ies, but the process was considered difficult for de-
velopers to implement - creating a cookie requires
the use of a language outside Flash (like JavaScript
or ASP). In the Flash MX version, Macromedia intro-
duced the Local Shared Object (LSO), which provides
an easier way to store information (i.e. only requires
the use of ActionScript).
LSOs provide the only method by which a Flash
application can store information on a user’s com-
puter. Intended uses of the object include storing a
user’s name, a favourite colour, or the progress in a
Works of (Nikiforakis et al., 2013), (Mayer and
Mitchell, 2012), and (Acar et al., 2014) show how
LSO can be used to track users, by performing a
browser fingerprinting.
The Electronic Privacy Information Centre
(EPIC) warns for the risks of identification of
individuals in an article regarding Local Shared
. According to EPIC “the Flash movie can
create a unique ID and store that ID in a Flash cookie
on a user’s computer. The Flash movie can then
communicate this information to a database, or other
applications. Subsequent visits of the same users
could be tracked by reading the ID contained in the
Flash cookie”.
Advantages: Flash cookies are a powerful way
to track users because they are still not properly
addressed by browsers and their management is not
trivial (i.e. management is not done together with
HTTP cookies). This lack of proper management
paves the way for exploiting this functionality for
tracking or fingerprinting.
Disadvantages: Because it requires the storing of
information, this technique is considered intrusive.
The use of this mechanism must abide to Article 5(3)
of Directive 2002/58/EC, amended by the Directive
2009/136/EC (also known as the ePrivacy directive),
which requires prior informed consent for storage or
access to information stored on a user’s equipment.
d) Web Storage (HTML5 Cookies): HTML5 intro-
duced two related mechanisms, similar to HTTP ses-
sion cookies, for storing name-value pairs on the cli-
ent side: sessionStorage and localStorage. Ac-
cording to the HTML Living Standard
: ”Storage ob-
ject provides access to a list of key/value pairs, which
are sometimes called items”.
Available at https://epic.org/privacy/cookies/flash.html
Available at https://html.spec.whatwg.org/
While sessionStorage is only stored during ses-
sion time and, therefore, has no useful application for
fingerprinting, localStorage, on the other hand, is
designed for storage that spans multiple windows, and
persists after the browser is closed.
Both, (Acar et al., 2014) and (Roesner et al.,
2012) refer to the use of the localStorage mechan-
ism as a way to perform browser fingerprinting.
Advantages: HTMLs localStorage might be a
concept somewhat obscure to most web users. The
functionalities of history, HTTP cookies and (normal)
cached content cleaning, available in most browsers
nowadays, might trick users into thinking that, once
used, all browsing content related data will be suc-
cessfully wiped from the system. Until browsers start
alerting the users of this data storing and provide a
simple mechanism to manage this type of data, users
will be exposed to the possibility of having a persist-
ent ID etched to their browser.
Disadvantages: In their nature, Flash’s Local Shared
Objects and HTMLs localStorage are cookies.
This means that the use of such mechanisms falls
under the Article 5(3) of Directive 2002/58/EC,
amended by the Directive 2009/136/EC, which re-
quires prior informed consent for storage or access to
information stored on a user’s terminal equipment. In
other words, websites using Flash or HTML5 cook-
ies must ask users if they agree with the storing of
data before the site starts to use them, risking penal-
ties when not abiding to these obligations.
4.9 Taxonomy
The taxonomy we present in Table 15 classifies each
technique according to the type of data that is collec-
ted. Whenever possible, categories were created to
group techniques according to the source of device-
related data that they explore (leftmost column). Ad-
ditional technique-related information is shown, such
as, the type of fingerprinting performed (browser,
cross-browser or both), if there is information written
to the client’s system (Active or Passive), and whether
the techniques rely on a comparison database to per-
form the fingerprinting.
It should be noted that the Flash retrieval of OS
features also comprises data about the Flash plu-
gin itself, therefore, providing information about the
browser (browser and cross-browser fingerprinting).
HTTP Header fields also allow both types of finger-
printing. This happens because properties, such as the
“User-Agent” field, provide both browser and system
SECRYPT 2016 - International Conference on Security and Cryptography
Table 15: Taxonomy of the web-based fingerprinting techniques.
Plugin enum. YES NO PASSIVE NO
Rounding pixels YES NO PASSIVE YES
Screen properties NO YES PASSIVE NO
Network interfaces NO YES PASSIVE NO
Font enumeration NO YES PASSIVE YES
Flash cookie YES NO ACTIVE NO
Due to its almost completely unnoticeable nature,
web-based fingerprinting raises several privacy is-
sues. The obvious one is the possibility to track a
user’s online behaviour without his consent. Web-
based fingerprinting also poses major risks to se-
curity: by collecting software versions (of browsers
and plugins) and operating system releases, a finger-
printer can gather enough information about a sys-
tem to perform a successful attack. This was the ba-
sic premise of the Blackhole Exploit Kit (BHEK) -
an exploit kit, created in 2010, designed to identify
software vulnerabilities in client machines commu-
nicating with it and exploiting discovered vulnerab-
ilities to upload and execute malicious code on the
client. The developer of computer security software,
Sophos, published a report (Howard, 2012) (p. 9)
about BHEK where it states that “[t]he purpose of the
landing page is straightforward: to fingerprint the ma-
chine. The landing page used by Blackhole uses code
from the legitimate PluginDetect library to identify:
OS, Browser (and browser version), Adobe Flash ver-
sion, Adobe Reader version, Java version”.
Sadly, the mitigation measures that exist today
fall short for what is needed. (Nikiforakis et al.,
2015) propose a “solution to the problem of browser-
based fingerprinting”, by modifying the browser to
make every visit appear different to a fingerprinting
site. However, this solution addresses only JavaScript
based techniques, like font/plugin enumeration and
screen resolution and, as shown in the current work,
there is a myriad of other techniques that PriVaric-
ator does not address, rendering it still fingerprint-
able. Tor Browser, another proposal for countering
fingerprinting, has several limitations and constraints.
The use of Tor networks adds specific constraints (e.g.
longer connection delays, script blocking) and risks
(e.g. confidentiality at the exit node), which would
render the benefits of its use pointless.
The work we present in this paper depicted a
study of the currently known web-based fingerprint-
ing techniques. A special effort was put into gather-
ing the most complete universe of techniques. Tech-
niques were categorized in a taxonomy that will al-
low readers to easily identify different types of tech-
niques, their capabilities, limitations and similarities.
The categories were defined bearing in mind that new
techniques might appear in the future and still have a
category where they will fit in.
Finally, this study provides a working basis for fu-
ture research in the field of web-based fingerprinting
mitigations by presenting a complete and understand-
able insight of the currently known techniques.
The Web-based fingerprinting concept emerged
after the realization that a device becomes unique be-
cause it is used by a unique entity-in this case, a human
being. As devices provide more customizations to
match the users preferences, the more individualized
they become, increasing the risk of fingerprinting.
Web-based Fingerprinting Techniques
This work is partially supported by National Funding
from FCT - Fundac¸
ao para a Ci
encia e a Tecnologia,
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