The 25 th International Conference on Auditory Display (ICAD 2019) 23–27 June 2019, Northumbria Uni versity
SURFING IN SOUND: SONIFICA TION OF HIDDEN WEB TRA CKING
Otto Hans-Martin Lutz abc , J acob Leon Kr ¨
oger ac , Manuel Sc hneiderbauer ad and Manfr ed Hauswirth abc
a W eizenbaum Institute for the Network ed Society , Berlin
b Fraunhofer FOKUS
Kaiserin-Augusta-Allee 31, 10589 Berlin, German y
{ otto.lutz, manfred.hauswirth } @fokus.fraunhofer.de
c T echnische Univ ersit
¨
at Berlin d Humboldt-Uni v ersit ¨
at Berlin
ABSTRA CT
W eb tracking is found on 90 % of common websites. It allows
online behavioral analysis which can re veal insights to sensiti ve
personal data of an individual. Most users are not aware of the
amout of web tracking happening in the background. This paper
contrib utes a sonification-based approach to raise user aw areness
by con ve ying information on web tracking through sound while
the user is bro wsing the web .
W e present a frame work for liv e web tracking analysis, con-
version to Open Sound Control e vents and sonification. The
amount of web tracking is disclosed by sound each time data is
exchanged with a web tracking host. When a connection to one
of the most prev alent tracking companies is established, this is
additionally indicated by a voice whispering the company name.
Compared to e xisting approaches on web tracking sonification, we
add the capability to monitor any network connection, including
all bro wsers, applications and de vices.
An initial user study with 12 participants showed empirical
support for our main hypothesis: exposure to our sonification sig-
nificantly raises web tracking aw areness.
1. INTRODUCTION
W eb tracking collects information about a particular user’ s activity
on the W orld W ide W eb . It is widely used, with some form of web
tracking found on 90 % of common websites, and on 60 % of web-
sites with highly priv acy-critical content [1]. Although complex
and extremely div erse, the ecosystem of web trackers is dominated
by a small number of companies, notably by Google, F acebook
and Amazon, who are inconspicuously present as third-party data
collectors on many websites [2]. Recent empirical results suggest
that third-party scripts owned by Google alone are present in about
80% of web traf fic of the top 600 websites, and are used in a track-
ing context in about 40 % [3].
Since a person’ s bro wsing beha vior re veals insights into his or
her personality , habits and sensitiv e aspects such as financial and
This work has been funded (in part) by the Federal Ministry of Education
and Research of Germany (BMBF) under grant no. 16DII111 (”Deutsches
Internet-Institut”).
This work is licensed under Creative Commons Attrib ution Non
medical situation or political vie ws, web tracking may constitute a
serious priv acy threat [4]. Even though web tracking is seen unfa-
v orably by the majority of internet users due to priv acy concerns
[5], they do not understand the full extent, the methods and possi-
bilities of online beha vioral tracking [6].
W eb tracking is in visible to the user by design. Studies show
that there is no suf ficient awareness of web tracking [7]. W e use
sonification of clandestine web traffic to tracking pro viders as a
means of raising aw areness for online pri vac y issues. If visualiza-
tion is used instead for the same objectiv e, users must di vert their
visual attention from their primary task (surfing the web). Using
the auditory domain, we can simultaneously communicate infor-
mation in a dif ferent modality , which provides additional attention
and workload resources [8]. Furthermore, sonification is suitable
to present temporal data in real-time and can be shaped to con ve y
emotional content [9, p.11, p.92].
Our contrib ution is a sonification-based approach to raise user
aw areness of web tracking which extends the possibilities of ex-
isting approaches like Soundbeam by Hutchins et al. [10]. W e
describe a framework for liv e web tracking analysis and con ver -
sion to OSC 1 e vents, which can be used to monitor web tracking
on any network connection – across all kinds of browsers, apps
and de vices. W e discuss our system, sonification and sound de-
sign. Finally , we present results of an initial user study with 12
participants. W e found empirical support for our main hypothe-
sis: exposure to the sonification significantly raised web tracking
aw areness.
2. RELA TED WORK
There is a comprehensiv e body of work on using sonification for
network traf fic monitoring to achie v e higher situational a wareness
in a network operations center (e.g., [11, 12], systematic o vervie w
in [13]). In this context, users are network security specialists
which use the auditory modality as supplementary resource to
achie ve their objecti ves in pattern, anomaly and intrusion detec-
tion. The scope of our approach, howe ver , focuses on the a verage
user who, in contrast to network operations professionals, is of-
ten unaware of the extent of web tracking [6]. Here, aw areness
refers to a general consciousness on the pre v alence of web track-
ing. Sonification of web tracking can increase this aw areness as it
Commercial 4.0 International License. The full terms of the License are 1 Open Sound Control, a network-based protocol for sound and media
av ailable at http://creativ ecommons.org/licenses/by-nc/4.0 control: http://opensoundcontr ol.or g
https://doi.or g/10.21785/icad2019.071 306

The 25 th International Conference on Auditory Display (ICAD 2019) 23–27 June 2019, Northumbria Uni v ersity
pro vides immediate auditory feedback to the user while he or she
is bro wsing the internet.
Soundbeam [10] sonifies third-party conne ctions e xtracted by
Mozilla Lightbeam, a plug-in for the Mozilla Firefox bro wser . It
sends data on intentionally visited websites and unintentionally
visited third-parties (e.g., analytics or adv ertisement pro viders) to
the SuperCollider synthesis engine via OSC. Soundbeam is de-
signed for ensemble performance. S e v eral users can run the soft-
w are on dif ferent computers in the same netw ork. When user B
encounters a third-party element that has been identified by user A
before, it is sonifi ed for both users. This is intended to “highlight
both the ubiqitousness and interconnectedness of tracking” [10].
Another related project is an earcon-based sonification of in-
ternet security threats for vision-impaired users [14]. Here, w arn-
ing sounds that con v e y their intended m eanings with little-to-no
user training (e.g., casting a fishing reel to w arn about a phish-
ing attack) were us ed to not ify users about security threats while
bro wsing on a screen reader .
3. FRAMEW ORK DESIGN
Our softw are runs in the background while the user is bro wsing
the web . The frame w ork comprises four st ages: (1) monitoring
netw ork traf fic, (2) filtering for connec tions to kno wn web track-
ers, (3) e xtracting dif ferent kinds of tracking-related e v ents, and
(4) sending these e v ents to the sound generator via OSC (see Fig-
ure 1).
Figure 1: System o v ervie w
In the prototyping phase, we used Ableton Li v e [16], with a
Max for Li v e OSC recei v er for sound synthesis. W e aim to switch
to cross-platform (Linux supported) open source softw are in the
future.
3.1. Implementation
In order to be able to intercept an y netw ork connecti on, we use
Python to c reate se v eral instances of TShark processes, a te xt-
based v ersion of the ne tw ork protocol analyzer wireshark [15].
These processes listen to the traf fic of the selected netw ork con-
nection. The y are configured wi th filter lists of web t rack er IP
addresses, so only traf fi c to these addresses is analyzed in the fol-
lo wing steps.
T rack er identification: Connections to track er services are
detected by track er identification lists a v ailable from dif ferent
sources (e.g., whotrac ks.me [17], easyList [18], or generated from
Mozilla Lightbeam). Each list has benefits and disadv antages.
F or our prototype, we used a semi-automated approach, access-
ing all Ale xa T op 50 W ebsites International and German y [19]
with Mozilla Lightbeam running in the background and e xport-
ing the list of third-parties accessed. When testing the l ists by
bro wsing random websites, this semi-automatically generated list
caught more third-party connections than the whotracks.me list.
On the other hand, the whotracks.me list supplies a dif ferentia-
tion between dif ferent cate gories of third-parties (e.g., adv ertising,
analytics, content deli v ery netw orks), which can pro vide a clearer
picture of the intentions behind the third-party connection. W e aim
to systematically compare dif ferent track er lists in the future.
Ev ent separation: W e configured TShark to listen to ports 80
(HTTP) and 443 (HTTPS) of the IP addresses generat ed from the
track er lists. W e spa wned separate TShark processes: a) monitor -
ing establishment of a connection (SYN e v ents) and b) monitoring
data transferred to track ers (GET / TLS application data e v ents).
W e further filter the SYN e v ents by connections to the top 10 most
pre v alent track ers t o further accentuate these acoustically (see Sec-
tion 3.2).
All these e v ents are stored in b uf fers and then sent out via
OSC. As sound e v ents which happen in close temporal proximity
are not discernible an ymore (precedence ef fect) [20], we send out
the b uf fered e v ents wi th a short pause in-between. In a heuristic
pre-test, a pause of 70 ms turned out to pro vide the best balance
between discering single e v ents and an o v erall coherent impres-
sion.
3.2. Sound design
The o v erall purpose of our approach is raising a w areness, creating
interest and stimulating tought on the topic of web tracking. The
auditory representation is designed to sho w the amount of web
tracking in the background, raise interest and con v e y some de gree
of danger in order to feature the associated pri v ac y concerns. Not
only the amount of tracking is important, b ut the f act that a group
of v ery fe w companies are present on most websites. Therefore,
we aim to disclose the oligopoly of these companies as well.
When a connection to one of the top 10 tracking companies is
established, we present an audio recording of the compan y’ s name
in a whispered manner . Re v erb is added to the whispers to inten-
sify the spatial and suspicious im pression, as a reference to the in-
trusion on pri v ac y . Some of the companies are well kno wn to users
(e.g., Google, F acebook), others are less kno wn (e.g., ComScore,
criteo). The whispered names are supposed to stimulate questions
about these companies as well.
Each data transfer e v ent is presented with a short sound e v ent.
The follo wing sound v ariations were designed for comparison re-
g arding users’ perception in terms of interest, curiosity , danger ,
and fear . W e aimed to design our sounds in a w ay to reflect either
po wer or fragility to con v e y both the po wer of tracking and the hid-
den, brittle quality it has as well. The po werful and fragile sounds
were designed both in a musical and an abstract sound v ariation.
Their numbers correspond to the sequence used in e v aluation.
1. po werful and mus ical: lo w cello and tuba
2. fragile a nd abstract: granular synthesis
3. po werful and abs tract V1: deep bleeps
4. fragile a nd musical: piccolo flute and violine
5. po werful and abs tract V2: lik e V1, added delay

307

The 25 th International Conference on Auditory Display (ICAD 2019) 23–27 June 2019, Northumbria Uni v ersity
A video containing both an impression of the sonic e xperience
with our system while surfing and e xamples of all sound v ariations
can be found at http://s.fhg .de/SonificationICAD2019 .
3.3. Comparison to existing appr oaches
Our approach of monitoring the internet traf fic itself instead of
relying on the Lightbeam br o wser plugin e xtends the capabilities
of Soundbeam by:
• supporting all bro wsers and combinations of ad / tracking
block er plug-ins.
• supporting monitoring of an y ph ysical or virtual netw ork con-
nection on the host computer . This enables monitoring traf fic
generated not only by web bro wsing b ut by apps as well.
• supporting monitoring the traf fic of an y de vice (e.g., laptop,
smartphone), if we open and monitor an ad-hoc wireless net-
w ork that this de vice connects to.
• usage and comparison of dif ferent track er blocking lists.
• con v e ying the name of the tracking compan y by whispers.
As we ha v e no means of identifying which addresses or links
the user w ants to visit, our approach does not support dif feren-
tiation between intentional website visits and third-party connec-
tions. Therefore, the quality of the track er identification list is an
essential f actor for a reliable result.
F or no w , we do not support ens emble performance as we cur -
rently aim t o mak e an indi vidual user a w are of the tracking he or
she personally is subjected to. T o create a multi-user e xperience,
the capability for sending OSC e v ents to dif ferent computers in the
netw ork can be added to our frame w ork.
4. EV ALU A TION
4.1. Study design and h ypothesis
W e conducted an initial user study with 12 participants (6 male,
5 female, 1 no gender stated) with an age range between 23 and
36 years, mean age w as 28.9 years. In a within-subjects design,
we presented the recordings of fi v e dif ferent sound v ariations in a
classroom setting. Each recording represented the sonification of
accessing the same website. It sho wed the actual sonic e xperience
while surfing, consisting of se v eral single bleeps occurring shortly
after each othe r . Whispering of the track er names w as muted in
order to set focus on the tonal quality of the sonified e v ents. Af-
ter each sound v ariation, participants filled out a questionnaire re-
g arding the percei v ed emotional qualities of the respecti v e sonic
e xperience. W e ask ed parti cipants to rate their o v erall auditory
impression of the sound playback (as if vis iting a website), not the
single sound elements. At the end, we presented all sound v aria-
tions ag ain and ask ed participants to state their f a v orite.
F or the emotional qualities of the sounds, we ask ed partici-
pants to rank each sound between the follo wing poles on a four -
point lik ert scale. F or statstical analysis, we assigned the numbers
(-2,-1, 1,2) to the scale items.
• innocent (-2) to dangerous (2)
• relaxing (-2) to frightening (2)
• boring (-2) to interesting (2)
• indif ferent (-2) to curious (2)
As we designed the system to raise a w areness, our main h y-
pothesis is that the a w areness re g arding web tracking gets higher
after e xposure to the sonification. W e assessed a w areness before
and after the sonification e xperience each with a fi v e-point lik ert
scale (lo w , rather lo w , medium, rather high, high).
Figure 2: Emotional content of the sound v ariations. Error bars in
plot: +/- one standard de viation
4.2. Results
As Shapiro-W ilk normality tests sho wed that normal distrib utions
cannot be assumed in our sample, we performed a one-sided
W ilcoxon signed rank test with continuity correction (see [21, p.
977]) to as sess the dif ferences between a w arene ss scores prior to
and after e xposition to the sonification. The test results support
our main h ypothesis: A w areness le v els were significantly higher
after e xposure to the sonification than before ( mean b ef or e =
0 . 75 , mean af t er = 1 . 25 , p = 0 . 024 , r =  0 . 652 ).
Results on the emotional qualities curiosity , interest, danger
and fear were less distinct and not significant (see T able 1 and
Figure 2). Hence, all stateme nts on the emotional qualities of the
sounds are descripti v e only . F or sound 1 (lo w cello and tuba),
danger and fear ratings were both high in mean and with a smaller
standard de viation compared to the other sounds. Interestingly ,
sound 4 (piccolo flute and violin) w as percei v ed least dangerous,
b ut raised the most curiosity . Sound 1 w as stated most often as
f a v orite (fi v e times), follo wed by sounds 4 and 5 (three times each).

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The 25 th International Conference on Auditory Display (ICAD 2019)
Sound v ariation: 1 2 3 4 5
mean(curiosity) 0 . 500 0 . 333 - 0 . 167 0 . 667 0 . 333
sd(curiosity) 1 . 168 0 . 985 1 . 267 0 . 778 1 . 231
mean(interest) 0 . 667 0 . 250 0 . 417 0 . 583 0 . 833
sd(interest) 1 . 073 1 . 138 1 . 311 0 . 996 1 . 193
mean(danger) 1 . 333 0 - 0 . 333 - 0 . 917 0 . 167
sd(danger) 0 . 492 1 . 279 1 . 435 0 . 996 1 . 267
mean(fear) 1 . 333 0 . 500 0 . 417 - 0 . 083 0 . 833
sd(fear) 0 . 492 0 . 905 1 . 084 1 . 165 0 . 937
T able 1: Sound v ariations: Means and standard de viations of emo-
tional content scores
5. DISCUSSION
The initial user study has limitations: Most notably , as the sounds
were presented in a classroom setting, a sequence ef fect is ex-
pected. Future studies will benefit from individual presentation via
headphones and randomisation of the sound v ariations. Adjectiv es
of the emotional quality poles were not selected from standardized
test batteries on emotional content. Additionally , the sample size
of 12 participants was quite small. Nev ertheless, some effect of
the sonification e xperience on web tracking a wareness could be
sho wn.
6. FUTURE RESEARCH
As our initial results are encouraging, we will continue and extend
our work in the follo wing ways: First, we aim to set it up in a
way that supports connecting a user’ s own device (laptop, smart-
phone) to a special wireless network we provide and monitor . By
this, we allow users to explore the tracking sounds of their o wn
bro wser or app configuration. W e are also looking into porting the
frame work to a small computer like the Raspberry Pi [22]. This
can ease the usage of our system in installations in public. Then,
we plan to conduct a larger user study that assesses the impact of
our approach to web tracking aw areness in the field.
Future research questions regarding sound design are mani-
fold: W e aim to disclose not only the amount of web tracking,
b ut the oligopoly of the tracking companies as well. So far , we
approached this with the tracker name whispering when connect-
ing initially . In future, we want to design signature sounds for
each compan y , so the corresponding single e vents can be linked to
these companies. Another significant step is moving on from pro-
ducing the sounds in Ableton Li v e to a model-based sonification.
Additionally , incorporating the spatial domain can help con v eying
tracker parameters by placement in the virtual room.
7. A CKNO WLEDGMENT
The authors w ant to thank Jan Maria K opankie wicz for his support
with implementation.
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