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The data flows that power today’s digital environment move across generations and cultures,
devices and software, businesses and households. Many of the currents that feed into this
environment are generated by the mobile app ecosystem, where users of all backgrounds
engage with their favorite games, social media, tools, and entertainment sources.
Teenagers are major participants in this ecosystem. And like everyone else interacting with
today’s connected world, teenagers are faced with questions and tradeos when it comes to
the use of mobile apps and privacy.
In policy discussions about privacy today, however, much of the dialogue focuses on the
privacy rights of citizens generally—sometimes through the vehicle of general privacy
regulation, and sometimes through protections for specific kinds of data, such as health data
or location data. Also common are discussions about prioritizing the privacy interests of
young children and whether existing legal frameworks, such as the Children’s Online Privacy
Protection Act
1
(COPPA), are adequate and sustainable in today’s digital world.
Risky
Business
The Current State of Teen Privacy in the Android App Marketplace
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Table of Contents
Key Findings 4
Teen apps have a greater attack surface for privacy risks. 4
Trackers observed most frequently appeared to be
controlled by Facebook and Google. 4
Teen users might be targeted based on their
in-app purchase spending behavior. 5
Why it Matters 5
Methodology 6
General Dataset 6
Teen Dataset 7
“Teen Directedness” 7
Genre of Teen Apps 8
Readability of Teen Apps 8
Word Frequencies, Teen Dataset 9
Monetization: Advertising vs. In-App Purchases 11
Identifying Third-Party Data Trackers 12
Analyzing App Permissions Requests 15
Dangerous Permissions Requests 16
Looking at the Global App Publisher Landscape 22
Conclusion 25
The unique privacy interests of teenagers, a significant part of the U.S.
population,
2
are rarely included in these privacy policy dialogues. This white
paper aims to address this shortcoming.
83%
of U.S. mobile
device owners aged
13 to 17 download an
app at least once a
month
3
81%
of teens use social
media with 70%
saying they use it
multiple times a day,
up from 34% in 2012
4
72%
of teens believe
that tech companies
manipulate users
to spend more time
on devices
5
89%
of teens have their
own smartphone,
more than doubling
since 2012
6
To protect teen privacy interests, some lawmakers are proposing to raise the protected age range
under COPPA to include teens
7
This year alone, we have seen the PROTECT Kids Act, the Kids
PRIVACY Act, and the KIDS Act each applying certain concepts from COPPA to children under the
ages of 16 or 17.
The ideas are worthy, but the specifics are lacking. The unique teen audience is strikingly dierent
than COPPAs current targeted age range of under 13. Defining a website, app, or platform as a
“teen space” is much more complex than identifying child-directed content. Teens occupy an
intermediate space between childhood and adulthood, which demands a nuanced approach to
setting new standards, not merely changing the age limit under COPPA.
Research shows that a teen’s drive for greater engagement on digital media platforms exposes
them to privacy risks.
8
There is a critical need to ensure that companies engaging teens in an
online environment collect data in a responsible manner, understand the unique teen audience,
and have the tools and support necessary to sustain responsible data collection in an evolving
regulatory atmosphere.
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The Missing Link in
Policy Discussions: Teens
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Key Findings
To assess the multiple privacy dimensions of more than 53,000 apps available in the
Google Play store, the apps were divided into two categories: apps directed to a general
audience, and a subset of those that were directed to teens.
The data indicate that Android-based apps likely directed to teens dier substantively
from general audience apps where information privacy is concerned.
Teen apps have a greater attack surface for privacy risks.
The teen dataset requested more permissions of its users and included more in-app
purchase options than apps in the general dataset. And in 9% of apps in the teen dataset
we were unable to identify a home country for the app publisher.
11
median permissions
requested per app
6
median “dangerous”
permissions requested
per app
10
median trackers
integrated into
each app
Trackers observed most frequently appeared to be controlled by Facebook and Google.
The Android mobile app ecosystem is complex, populated by a variety of publishers that
often rely on third-party software to monetize their apps through advertising or in-app
purchases.
Apps function alongside powerful sensor, storage, and tracking technologies engineered
into smartphones. Google, the owner of the Android platform and a powerful advertising
network, and Facebook, known for its social media networks, dominate this market through
myriad tracking and ad products. Such major players are woven together with innumerable
advertising technology companies to create an interlocking mesh of data exchange that
funds an enormous swath of internet services.
Many consumers, including teens, engage with their favorite apps every day
unaware of the hidden ecosystem that drives them.
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Teen users might be targeted based on their in-app purchase spending behavior.
The teen dataset included more ad-supported apps than the general dataset.
82%
of teen-directed apps
were supported by ads, compared to
51%
of general apps
Games for teens were more likely to have in-app purchases than game apps in the general
dataset.
General Dataset
4:1
Teen Dataset, almost
13:1
Why it Matters
Apps targeted to teenage users are more likely to engage in ad serving, include more
third-party trackers, ask for more permissions, and oer more in-app purchases.
App developers, advertisers, and third-party technology companies routinely focus more
heavily on monetizing teenage users, through advertisements and in-app purchases, as
compared to general audience users. In addition, the teen dataset included more third-party
trackers and requested more permissions to data, including those defined as “dangerous”
permissions. While many app developers appear to abide by the Google Play Developer
policies about privacy, our study suggests there are important questions about whether they
collect or authorize the collection of excessive amounts of data.
ratio of game apps with in-app
purchases to those without
ratio of game apps with in-app
purchases to those without
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And because Google and Facebook own the majority of trackers in teen apps, this report
demonstrates how platforms such as these can combine data, collected across a variety
of apps, to create a full profile of single users – what they look like, what they sound like,
where they go, who their friends and family are, where they work and live, their daily habits
and interests, and even the contents of their phone.
In a world where teens are restricted from driving, voting, and making other decisions
regarding their autonomy, why does the assumption exist that they can properly manage
their own data privacy?
In today’s landscape, should teens be required to understand and be mindful of the
inherent tradeos that exist between the data collection and advertising practices in the
mobile apps they use and their own personal privacy?
Although the proposed laws that expand the scope of COPPA to include some teenagers
are well-intentioned, we should not treat teens the same way we treat children, nor should
we treat them as fully developed adults.
Given our findings and the potential rigidity of future legislative solutions, it is incumbent on
industry now to exercise responsibility and show accountability by developing appropriate
standards that take into account teens’ habits, preferences, and developmental state.
This study is brought to you by BBB National Programs’ TeenAge Privacy Program (TAPP),
a community of companies that understand the digital landscape and the complications of
standards implementation that will develop the core principles and standards necessary for
teen privacy.
Methodology
This study analyzes privacy issues surrounding the Android mobile app ecosystem, with a
focus on mobile apps directed to teenagers.
9
In conducting this study, we aimed to survey
the landscape of teen mobile apps for privacy risks and assess how teen data is harvested
by the products they use.
TAPP’s dataset for this paper is based on a general dataset and a teen dataset.
General Dataset
To create this dataset, we scraped data from the top 200 apps for each genre
10
in the
Google Play Store, yielding an initial set of 11,338 apps with accompanying data.
11
We then
expanded this dataset to include all apps marked by Google Play as similar
12
to the initial
set of 11,338 apps, yielding a total of 53,686 apps, and scraped app data from the Google
Play Store to include the entire expanded dataset
13
This scraped data included a list of
permissions requested, indicators as to monetization through advertising and in-app
purchases, installation counts, links to privacy policies, and in some cases the location of
the app publisher.
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Teen Dataset
To create this dataset, we identified popular apps (apps which had been installed 20 million
or more times) with characteristics likely directed at teenagers. This process yielded a list
of 1,322 apps.
To further narrow this sample, we built a multi-factor framework for assessing whether
an app was reasonably classified as directed at teens. To construct this framework,
we adapted industry standards for marketing to teens, motion picture and software
rating guidelines, FTC parameters for assessing the child-directed nature of content for
compliance with COPPA, and general knowledge about popular teen products in 2020.
14
Using this framework as a reference, we manually narrowed our sample to 1,156 teen-
directed apps. After identifying these apps, we downloaded the Android application
package (APK)
15
of each.
16
Accounting for minor adjustments and download failures, the
final list for the teen dataset consisted of 1,144 apps with accompanying APKs.
For each of these datasets, where possible, we assessed multiple privacy dimensions of
each app.
The presence and number of permissions each app requested
The number of trackers integrated into each app
The readability of each app’s description in the app store
The presence of each app’s privacy policy
Whether each app monetizes through advertising or in-app purchases
“Teen Directedness”
In addition, we measured the teen dataset and compared it with the general dataset to
confirm that our filtering processes produced a list of teen-focused apps. We looked at
three points of comparison:
1. Does the genre breakdown change between teen and general datasets? We expected,
for example, to see fewer utility apps, business apps, etc. in the teen dataset.
2. Using an appropriate readability scoring formula, is the reading level of the app
descriptions on the Google Play Store lower for apps from the teen dataset than for the
general dataset? We expect the descriptions of apps targeted at a general audience to
score at a higher reading level than the descriptions of apps targeted at teens.
3. Which words predominate in the descriptions? We expected teen-directed apps to use
dierent words in their descriptions as compared with general apps, and we further
expected the most common words to be easily associable with teens, their behavior, or
their interests.
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Genre of Teen Apps
TAPP’s teen dataset included popular apps known to include teen users, (e.g., TikTok,
Instagram, Facebook, Snapchat), gaming, social media, entertainment, messaging, and
file sharing. Notably, gaming apps
17
consisted of 55.8% of our teen dataset but only
30.1% of our general dataset.
Table 1.
Genre Breakdown, Top 10, General Dataset
GENRE
Number
of apps
Tools 3558
Education 3188
Health & Fitness 2609
Puzzle Games 2306
Productivity 2130
Lifestyle 1936
Finance 1872
Business 1805
Music & Audio 1712
Entertainment 1677
Table 2.
Genre Breakdown, Top 10, Teen Dataset
GENRE
Number
of apps
Action Games 120
Casual Games 88
Photography 79
Video Players & Editors 70
Arcade Games 64
Tools 59
Puzzle Games 57
Sports Games 56
Music & Audio 55
Entertainment 53
Readability of Teen Apps
When the app’s descriptions were assessed against the Dale-Chall readability formula,
18
which relies on 3000 “familiar words” that are known by 80% of children in the 5th
grade, we observed that the average app description for the teen dataset scored
approximately 9, while the average app in the general dataset scored 9.59.
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adjusted Score = Raw Score + 3.6365 (if dicult words more than 5%)
Score Notes
4.9 or lower easily understood by an average 4th-grade student or lower
5.0–5.9 easily understood by an average 5th or 6th-grade student
6.0–6.9 easily understood by an average 7th or 8th-grade student
7.0–7.9 easily understood by an average 9th or 10th-grade student
8.0–8.9 easily understood by an average 11th or 12th-grade student
9.0–9.9 easily understood by an average 13th to 15th-grade (college) student
Finance 1872
Business 1805
Music & Audio 1712
Entertainment 1677
Table 3:
Reading Level for Dataset App Descriptions
Dataset Dale-Chall Score
Teen dataset 9.03
General dataset 9.58
General dataset
minus teen
dataset
9.59
7.0–7.9 easily understood by an average 9th or 10th-grade student
8.0–8.9 easily understood by an average 11th or 12th-grade student
Word Frequencies, Teen Dataset
We also analyzed word frequency counts in the summaries and descriptions scraped from
the Google Play Store for both the teen dataset and the general dataset (with the apps
in the teen dataset removed from the general dataset for this comparison), removing
punctuation and connective words (e.g., “and,” “the,” “his,” etc.), and consolidating
word forms (e.g., “game” includes “gaming,” “games,” “gamer,” etc.). By comparing the
frequently occurring words in each dataset, we were able to determine whether the teen
dataset predominantly included words directed to teens, or words indicative of their
behavior or interests, relative to the general dataset.
The results confirmed the hypothesis that the teen dataset included words associated with
teens and their interests. The following tables illustrate this, showing the most common 30
words in each dataset. While many words common to popular apps (such as “game”) are
highly ranked in both datasets, in the teen dataset words such as “challenge,” “music,” and
“battle” all rank among the most commonly occurring words but are not found among the
most common words in the general dataset. In the general dataset, the predominant words
indicate a more utilitarian outlook, such as “learn” and “easy.
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This study examines the current state of privacy in the Android teen app marketplace and
breaks down the privacy risks that teens face with each engagement.
Table 4:
30 Most Common Words, Teen Dataset
Word Count
Game 3998
video 3044
app 2287
play 2182
free 1884
photo 1859
new 1624
use 1509
friend 1455
feature 1329
get 1325
music 1297
make 1282
download 1170
world 1076
best 1030
player 1022
like 962
share 891
fun 848
time 831
create 816
mode 813
one 770
device 765
take 765
support 724
challenge 690
eect 687
battle 644
Table 5:
30 Most Common Words, General Dataset
Word Count
app 114024
game 108873
use 66293
free 55505
new 53043
play 48548
feature 45705
get 44045
make 39100
time 38804
help 31556
find 30483
video 30392
support 29451
best 28568
device 28241
one 27332
like 27273
world 26529
learn 26033
create 25193
photo 24550
need 24029
phone 23059
download 22376
easy 22025
also 21465
fun 20978
include 20889
share 20141
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Monetization: Advertising vs. In-App Purchases
TAPP further examined the number of apps in each dataset that relied on advertising
or in-app purchases for monetization. In the mobile app ecosystem, app publishers
frequently rely on advertising in the form of contextual advertising
19
or interest-based
advertising (IBA, also known as targeted advertising)
20
to monetize their products.
Additionally, app publishers may integrate the ability to purchase items, features,
and upgrades into their apps (in-app purchases or IAP),
21
We note that our datasets
and our program’s general experience in the mobile app ecosystem show that many
mobile apps employ both methods.
In the teen dataset, almost 83% of apps used advertising to monetize, compared to
51% in the general dataset.
In the teen dataset, 78% of apps contained in-app purchases, compared with less than
50% in the general dataset.
When looking at the genres of gaming apps, games for teens were more likely to
have in-app purchases. In the teen dataset, the ratio of apps with in-app purchases to
those without was almost 13:1, compared to the general dataset’s ratio of 4:1.
Table 6:
Ad-Supported Apps, General Dataset
Advertising Monetization
Number
of Apps
Ad supported 27,335
Non-ad supported 26,351
Table 7:
Ad-Supported Apps, Teen Dataset
Advertising Monetization
Number
of Apps
Ad supported 948
Non-ad supported 196
Table 8:
In-App Purchase Apps, General Dataset
In-App Purchases
Monetization
Number
of Apps
In-app purchases 26,599
No in-app purchases 27,087
Table 9:
In-App Purchase Apps, Teen Dataset
In-App Purchases
Monetization
Number
of Apps
In-app purchases 895
No in-app purchases 249
Table 10:
In-App Purchase Gaming Apps, General
Dataset
Gaming Apps
Number
of Apps
In-app purchases 12,882
No in-app purchases 3,254
Table 11:
In-App Purchase Gaming Apps, Teen
Dataset
Gaming Apps
Number
of Apps
In-app purchases 591
No in-app purchases 47
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Identifying Third-Party Data Trackers
We used Exodus Privacy’s Exodus Core framework
22
to identify third-party trackers in
downloaded APKs.
23
Broadly speaking, a tracker is a type of software intended to collect
data about user behavior. A tracker can be a “first-party” tracker, created by an app
publisher, or a “third-party” tracker, created by an unaliated company and integrated by
the publisher.
In mobile apps, trackers commonly collect device identifiers, location data, or other
characteristics of a mobile device’s hardware or software to facilitate data collection for
IBA, a common means of monetizing mobile apps and websites.
24
We disassembled the downloaded APKs from the teen dataset to analyze the “bytecode”
of the APKs for the signatures of known trackers and found:
As seen in Figure 1, the median number of trackers per app was 10.
As seen in Table 13, the most common tracker was Google AdMob.
Most trackers are owned by Facebook or Google.
As seen in Table 14, many of the apps with the highest number of trackers are oered
by the Cyprus-based app publisher Outfit7.
25
As seen in Table 15, Action games have the largest number of aggregate trackers.
As seen in Table 16, when gaming apps are taken out of the equation, photography
apps have the highest aggregate number of trackers.
When we examine genres with 15 or more apps in this dataset, word games have the
highest tracker count when calculated by median (17), followed by arcade games (14).
When gaming apps are taken out of the equation, when we examined genres with 15
or more apps in this dataset, we found that social media apps have the highest tracker
count when calculated by median (11), followed by photography apps (9).
Figure 1:
Tracker Presence Per App, Teen Dataset
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Table 13:
Top 10 Trackers, Teen Dataset
Tracker Name Number of Apps
Google AdMob 1086
Google Firebase Analytics 981
Facebook Ads 672
Facebook Login 614
Facebook Share 594
Google CrashLytics 580
Facebook Analytics 579
Unity3d Ads 567
Moat (Oracle) 498
AppLovin 402
Table 14:
Apps with Highest Number of Trackers, Teen Dataset
App Name Number of Trackers
Magic Jigsaw Puzzles 47
My Story: Choose Your Own Path 38
Perfect 365: One Tap Makeover 37
Rodeo Stampede: Sky Zoo Safari 37
Rayman Adventures 36
My Talking Hank 35
Text Me: Text Free, Call Free, Second Phone Number 35
Smurfs’ Village 34
Swamp Attack 34
Talking Ben the Dog 34
International Fashion Stylist: Model Design Studio 33
My Talking Tom 33
Talking Tom Cat 33
AXES.io 33
Talking Ginger 2 32
My Talking Tom 2 32
My Talking Tom Friends 32
Talking Angela 32
Talking Tom Candy Run 32
Talking Tom Gold Run 32
Talking Tom Jetski2 32
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App Name Number of Trackers
Talking Tom Hero Dash 31
My Talking Angela 31
Checkers 31
ASKfm-Ask Me Anonymous Questions 30
Talking Ginger 30
Talking Tom and Ben News 30
textPlus: Free Text & Calls 29
Talking Tom Bubble Shooter 29
battle 644
Table 15.
Trackers by Genre, Teen Dataset
Genre Aggregate Trackers Number of Apps Median Trackers
Action Games 1,260 120 9
Casual Games 1,162 88 12
Arcade Games 881 64 14
Puzzle Games 779 57 13
Photography 661 79 9
Sports 644 56 11
Entertainment 567 53 9
Music & Audio 537 55 8
Video Players & Editors 535 70 7
Racing Games 518 48 10
Table 16.
Trackers by Genre, Games Removed, Teen Dataset
Genre Aggregate Trackers Number of Apps Median Trackers
Trackers 1,162 88 12
Photography 661 79 9
Entertainment 567 53 9
Music & Audio 537 55 8
Video Players & Editors 535 70 7
Tools 449 59 6
Social 390 31 11
Communication 245 37 6
Personalization 191 21 8
Health & Fitness 169 22 8
Shopping 102 11 11
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Analyzing App Permissions Requests
Mobile apps can request permission to access certain device features such as a Global
Positioning System (GPS) location, microphone, and/or camera. Permissions requests
typically come in the form of a pop-up box that requires the user to either enable or
disable the request, while other permissions are enabled automatically in the app, such as
permissions for internet access (see Figure 2).
26
Google states that “the purpose of a permission is to protect the privacy of an Android
user.
27
Google Play store policies indicate that apps should not ask permission to access
something that they do not need to function (e.g., an e-reading app should not need to
request a location permission to function).
Figure 2:
Example – Google App Permission Request
To conduct our analysis of permissions requests, we relied on both public data scraped
from the Google Play Store and data that we obtained from each app’s manifest
28
as a
result of our static analysis. Wherever possible, we relied on the manifest permissions list
that we analyzed in each app’s APK. However, to obtain equivalent data when comparing
general and teen datasets, we used permissions data scraped from the Google Play Store.
This is because the Google Play Store data and the information contained in an app’s
manifests may dier.
Based on the data listed in the Google Play Store, apps in the teen dataset requested
a median of 11 permissions per app compared to 10 permissions per app in the general
dataset.
In our static analysis, conducted of the apps in the teen dataset, we found the median
number of permissions requested in the teen dataset was 8 (Figure 3).
29
282 apps requested 6-7 permissions, the mode of the teen dataset (Figure 3).
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5 apps requested more than 50 permissions each, with 2 of those apps requesting over 100
permissions each (Figure 3).
Apps from the teen dataset in the “Tools” genre were found to have the largest number of
aggregate permissions requests, followed by communication apps (Table 21).
Dangerous Permissions Requests
The Google Play Store categorizes permissions requests into three categories: normal,
signature, and dangerous. Normal permissions requests pose little threat to user privacy,
whereas dangerous permissions “involve the user’s privacy information or could potentially
aect the user’s stored data or the operation of other apps.
30
” For example, accessing a
device’s fine location or camera would be considered dangerous permissions requests.
When examining publicly scraped data from the Google Play Store, we found that there
was a median of 5 dangerous permissions requested for apps in the general dataset
compared to a median of 6 dangerous permissions requested in the teen dataset.
Looking at permissions requests data from the teen dataset’s app manifests, we identified
the top 10 dangerous permissions requested across teen-directed apps as well as the top 5
trackers associated with apps requesting those dangerous permissions (Tables 19 & 20).
Though the permissions requests are visible to the user, the trackers themselves are often
not. When comparing which trackers appeared with which permissions a specific app
requested, adjusting for how many times a tracker appeared overall in the teen dataset, we
identified the top 5 trackers associated with each common dangerous permission request.
Echoing the earlier tracker analysis, most trackers identified as frequently co-occurring
with dangerous permissions are controlled by Facebook and Google.
Figure 3:
Permissions Requested per App, Teen Dataset
Table 17:
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Top 10 Requested Permissions, Teen Dataset
Permission Name Function
Protection
Level
Number of
Apps
INTERNET Allows applications to open network
sockets
Normal 1139
ACCESS_NETWORK_
STATE
Allows applications to access
information about networks
Normal 1136
WAKE_LOCK Allows using PowerManager
WakeLocks to keep processor from
sleeping or screen from dimming.
Normal 1072
WRITE_EXTERNAL_
STORAGE
Allows applications to write to
external storage
Dangerous 851
ACCESS_WIFI_STATE Allows applications to access
information about Wi-Fi networks
Normal 832
VIBRATE Allows access to the vibrator. Normal 701
READ_EXTERNAL_
STORAGE
Allows applications to read from
external storage. Any app that
declares the WRITE_EXTERNAL_
STORAGE permission is implicitly
granted this permission.
Dangerous 697
RECEIVE_BOOT_
COMPLETED
Allows applications to receive the
Intent.ACTION_BOOT_COMPLETED
that is broadcast after the system
finishes booting.
Normal 485
FOREGROUND_
SERVICE
Allows a regular application to use
Service.startForeground.
Normal 359
CAMERA Required to be able to access the
camera device.
Dangerous 294
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Table 18:
Apps Requesting the Greatest Number of Permissions, Teen Dataset
Word Count
Parallel Space – Multiple accounts & Two face 102
Parallel Space Lite – Dual App 102
Samsung Smart Switch Mobile 66
EasyShare – Ultrafast File Transfer, Free & No Ads 60
GO Launcher - 3D parallax Themes & HD Wallpapers 52
Truecaller: Caller ID, block fraud & scam calls 47
Signal Private Messenger 47
Messenger for SMS 46
WeChat 46
Free phone calls, free texting SMS on free number 45
TextNow: Free Texting & Calling App 43
AirDroid: Remote access & File 43
UC Browser- Free & Fast Video Downloader, News App 41
C launcher:DIY themes,hide apps,wallpapers,2020 40
KakaoTalk: Free Calls & Text 40
GO Security—AntiVirus, AppLock, Booster 39
Mi Browser Pro - Video Download, Free, Fast&Secure 39
WhatsApp Messenger 39
OK 39
Skype - free IM & video calls 38
APUS Launcher - 3d wallpaper&Themes,Hide apps 37
Safe Security - Antivirus, Booster, Phone Cleaner 37
UC Mini-Download Video Status & Movies 37
Uplive - Live Video Streaming App 36
Facebook 36
SHAREit - Transfer & Share 36
Mi Remote controller - for TV, STB, AC and more 35
BOTIM - Unblocked Video Call and Voice Cal 35
Eyecon: Caller ID, Calls and Phone Contacts
34
AppLock 34
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As seen above, we identified 5 apps requested over 50 permissions each, with 2 of those
apps requesting over 100 permissions each.
Table 19:
Top 10 Requested Dangerous Permissions, Teen Dataset
Permission Name Function
Number of
Apps
WRITE_EXTERNAL_STORAGE Allows app to write to external storage 851
READ_EXTERNAL_STORAGE Allows app to read from external
storage. Any app that declares the
WRITE_EXTERNAL_STORAGE
permission is implicitly granted this
permission.
697
CAMERA Required to be able to access the
camera device.
294
RECORD_AUDIO Allows app to record audio. (Enables the
device microphone)
265
READ_PHONE_STATE Allows read-only access to phone state,
including the current cellular network
information, the status of any ongoing
calls, and a list of any PhoneAccounts
registered on the device
281
ACCESS_COARSE_LOCATION Allows app to access approximate
location.
225
GET_ACCOUNTS Allows access to the list of accounts in
the Accounts Service.
216
ACCESS_FINE_LOCATION Allows app to access precise location 201
READ_CONTACTS Allows app to read the user's contacts
data.
128
WRITE_CONTACTS Allows app to write the user's contacts
data.
50
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Table 20:
Top 5 Trackers Associated with Dangerous Permissions, Teen Dataset
Permission Name Associated Trackers
WRITE_EXTERNAL_STORAGE Google Crashlytics
Facebook Login
Facebook Share
Facebook Analytics
Moat
READ_EXTERNAL_STORAGE Google Crashlytics
Facebook Login
Facebook Share
Facebook Analytics
Moat
CAMERA Facebook Share
Facebook Login
Google Crashlytics
Facebook Analytics
AppsFlyer
RECORD_AUDIO Facebook Share
Facebook Login
Facebook Analytics
AppsFlyer
Google Crashlytics
ACCESS_COARSE_LOCATION Facebook Share
Facebook Login
Facebook Analytics
Google Grashlytics
Twitter MoPub
ACCESS_FINE_LOCATION Facebook Login
Facebook Share
Facebook Analytics
Google Analytics
Facebook Places
READ_CONTACTS Google AdMob
Google Firebase Analytics
Facebook Ads
Facebook Login
Facebook Share
READ_PHONE_STATE Facebook Login
Facebook Analytics
Facebook Share
Google Crashlytics
Facebook Places
GET_ACCOUNTS Facebook Login
Facebook Share
Google Crashlytics
Facebook Analytics
Facebook Places
WRITE_CONTACTS Google AdMob
Google Firebase Analytics
Facebook Ads
Facebook Login
Facebook Share
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Table 21:
Top 10 Genres Requesting Most Permissions, Teen Dataset
1. Tools
2. Communication
3. Photography
4. Video Players & Editors
5. Action Games
6. Social
7. Music & Audio
8. Casual Games
9. Entertainment
10. Personalization
Table 22:
Top 10 Genres by Number of Permissions Requested, Teen Dataset
App Genre
Aggregate
Permissions
Number of
Apps
Median
Permissions
Tools 1211 59 17
Communication 1090 37 32
Photography 925 79 10
Video Players & Editors 853 70 10
Action Games 830 120 6
Social 782 31 23
Music & Audio 743 55 13
Casual Games 586 88 6
Entertainment 584 53 9
Personalization 494 21 18
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Table 23:
Top 10 per Genre by Number of Permissions Requested, Games Removed, Teen Dataset
App Genre
Aggregate
Permissions
Number of
Apps
Median
Permissions
Tools 1211 59 17
Communication 1090 37 32
Photography 925 79 10
Video Players & Editors 853 70 10
Social 782 31 23
Music & Audio 743 55 13
Entertainment 584 53 9
Personalization 494 21 18
Health & Fitness 305 22 11
Productivity 217 15 15
Apps from our teen dataset in the “Tools” genre have the largest number of aggregate
permissions, followed by communication apps. When we examine genres with 15 or more
apps in our dataset, we find that communications apps request the greatest median
number of permissions, followed by social media apps.
Looking at the Global App Publisher Landscape
Starting with the information provided in each Play Store profile, TAPP correlated each app
in the teen dataset to the country where its developer is based
31
to identify the countries
32
with the greatest number of apps in the dataset and whose correlating apps contained the
most trackers. Notably, approximately 9% of apps in the teen dataset did not provide any
clear address information for their developers, which required us to conduct independent
research to identify these developers’ home countries.
Using this information, we identified:
Countries with app publishers with the highest tracker count (Table 25).
The countries with apps that requested the most permissions (Table 26).
In-app purchase percentages for top app publisher countries (Figure 5).
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Table 24:
Countries with Greatest Number of Apps, Teen Dataset
Country Number of Apps
United States 342
China 119
United Kingdom 54
South Korea 46
Singapore 43
France 39
Cyprus 37
Israel 36
Russia 30
India 29
Figure 4:
Heat Map of Countries with Most Apps, Teen Dataset
On the next page, Table 25 illustrates the median number of trackers per app associated
with each country. To provide a more objective look at the overall privacy impact of each
country’s app publishers, we set a threshold for each country excluding any with fewer than
30 associated apps. We further provide in Table 26 the countries with the most permissions
requested per app from the teen dataset.
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Table 25:
Countries with Most Trackers per App, Teen
Dataset
Country
Median
Trackers
Cyprus 28
Israel 17
France 13
United Kingdom 11
China 10
United States 9
Singapore 9
Russia 7
South Korea 6
Table 26:
Countries with Most Permissions Requested
per App, Teen Dataset
Country
Median
Trackers
China 18
South Korea 15.5
France 15
United States 14
Singapore 11
Russia 10
Israel 10
United Kingdom 9
Cyprus 9
Figure 5:
In-App Purchases Percentages, Teen Dataset
Here, in Figure 5 above, we observe that a high percentage of each country’s app products
monetize through in-app purchases.
33
We speculate that app developers aim to maximize
their monetization through a combination of data collection for advertising and in-app
purchases, especially if they oer a product that achieves a high installation count.
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Conclusion
This study demonstrates an unchecked ecosystem of data collection for a uniquely
vulnerable audience. Teen data privacy is a complex topic that does not belong in the same
conversations as data privacy regulation for children. As the data show, teens are voracious
users of digital media platforms, and their quest for greater engagement threatens the
safety of their personal data.
We look forward to more opportunities to continue this research and encourage companies
interested in joining the eorts of the TeenAge Privacy Program to reach out and contact
us at TAPP@bbbnp.org.
______________
[1] See generally Children’s Online Privacy Protection Act of 1998, 15 U.S.C. 6501–6505. See also Federal Trade
Commission, Children’s Online Privacy Protection Rule (“COPPA”), https://www.ftc.gov/enforcement/rules/
rulemaking-regulatory-reform-proceedings/childrens-online-privacy-protection-rule (last visited Sept. 16, 2020).
[2] See Marketing Charts, US Population Distribution, by Age Group and Gender (July 1, 2019), https://www.
marketingcharts.com/charts/us-population-distribution-by-age-and-gender-in-2019/attachment/censusbureau-
us-population-distribution-by-age-group-and-gender-july2020, noting individuals between 12-17 year old
represent 7.6 percent of the total population.
[3] J. Clement, U.S. mobile device owner monthly app download rate 2018, by age group, Statista, (Dec. 6, 2019),
https://www.statista.com/statistics/243794/us-adult-cell-phone-owners-who-have-downloaded-apps-by-age-
group/.
[4] Rideout, V., and Robb, M.B., Social Media, Social Life: Teens Reveal Their Experiences, Common Sense Media
(2018), https://www.commonsensemedia.org/sites/default/files/uploads/research/2018-social-media-social-life-
executive-summary-web.pdf.
[5] Id.
[6] Id.
[7] See generally, Congress.gov, H.R.5573 - PROTECT Kids Act, https://www.congress.gov/bill/116th-congress/
house-bill/5573?s=1&r=1 (last visited Oct. 22, 2020). Markey.senate.gov, SENATORS MARKEY AND BLUMENTHAL
INTRODUCE FIRST-OF-ITS-KIND LEGISLATION TO PROTECT CHILDREN ONLINE FROM HARMFUL CONTENT,
DESIGN FEATURES (March 5, 2020),https://www.markey.senate.gov/news/press-releases/senators-markey-
and-blumenthal-introduce-first-of-its-kind-legislation-to-protect-children-online-from-harmful-content-
designfeatures_#:~:text=and%20Senator%20Richard%20Blumenthal%20(D,and%20Safety%20(KIDS)%20
Act.&text=%E2%80%9CBig%20Tech%20has%20designed%20their,purchases%2C%E2%80%9D%20said%20-
Senator%20Blumenthal.
[8] Pew Research Center, Teens, Social Media, and Privacy (May 21, 2013), https://www.pewresearch.org/
internet/2013/05/21/teens-social-media-and-privacy/.
[9] We note that due to our research methodology it was easier to narrow the focus of this paper to the Android
mobile app ecosystem. We do not intend to provide commentary on the privacy practices of Apple and Google in
relation to one another. Nothing in this report is intended or should be construed to be an endorsement or critique
of a company’s policies or practices.
[10] See generally Google Play Store, Top Charts, https://play.google.com/store/apps/top?hl=en_US (last visited
Sept. 9, 2020). Genres can be access by clicking “categories” option. We note that genres include, but are not
limited to, the following: ‘Dating,’ ‘Education,’ ‘Entertainment,” ‘’Finance,’ etc.
[11] See generally Google-play-scraper, https://github.com/facundoolano/google-play-scraper/ (last visited Sept.
8, 2020).
[12] TAPP notes that a similar app is an app linked on the sidebar of a Google Play Store app page under “similar.”
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[13] This figure is the final number of apps for our general dataset after some errors were removed.
[14] The Digital Advertising Accountability Program relied on the following framework to assess the teen-directed
nature of apps. (1) The subject matter, including childish pranks, rites of passage, youth romance, fashion, beauty,
sports, performing arts, or mental health. (a) Subject matter appealing primarily to children, such as basic spelling
or counting, elementary school arithmetic, most coloring books, and low-vocabulary apps, is not considered to
be directed at teenagers. (b) Subject matter chiefly of interest to or appropriate only for adults, such as business,
finance, gambling, or pornography is not considered to be directed at teenagers (i) Notwithstanding factor (1)
(b) of these guidelines, business or utility-oriented apps that provide features of strong interest to teenagers,
such as file sharing or video downloading, may be considered as directed to teenagers, even though such apps
may appear to be targeted primarily to adults. (2) Visual content, including settings appealing to teenagers
such as skate parks, zoos, summer camps, or teen clubs. (a) Visual depictions targeted at teenagers may include
depictions of violence, suggestive themes, crude humor, minimal blood, and some strong language. (b) Visual
content appealing to young children, such as brightly colored animated creatures, fairy tale settings, or other
images or activities associated with young children, is not considered to be directed to teenagers. (c) Visual
content chiefly of interest to or appropriate only for adults, such as intense violence, blood and gore, and sexual
content is not considered to be directed to teenagers. (3) The use of teenager-oriented characters, activities, or
incentives, particularly involving characters under the age of 25. (4) The kind of music or other audio content.
(5) Models appearing to be under the age of 25. (6) The presence of celebrities who appeal to teenagers. (7)
Language or other characteristics of the app. (8) Whether advertising that promotes or appears on the app is
directed to teenagers. (9) Notwithstanding the prior sections of these guidelines, apps which are known to be
popular among teenage users may be considered as directed to teenagers, even though such apps may appear to
be targeted primarily to other users. We note that this framework was influenced by the Entertainment Software
Ratings Board ratings, factors the FTC has established for examining, the child-directed nature of content, https://
www.ftc.gov/tips-advice/business-center/guidance/complying-coppa-frequently-asked-questions-0, standards for
ratings set by the Motion Picture Association of America, https://www.filmratings.com/downloads/rating_rules.
pdf, and the EU’s Responsible Marketing Pact for avoiding the exposure of minors to advertising for alcoholic
beverages, https://www.filmratings.com/downloads/rating_rules.pdf. See generally Entertainment Software
Ratings Board, Ratings, https://www.esrb.org/ratings/ (last visited Sept. 8, 2020), Federal Trade Commission,
Complying with COPPA: Frequently Asked Questions, § D.1., https://www.ftc.gov/tips-advice/business-center/
guidance/complying-coppa-frequently-asked-questions (last visited Sept. 8, 2020). FTC v. TinyCo, FTC Matter
132 3209 (Sept. 2014), https://www.ftc.gov/system/files/documents/cases/140916tinycocmpt.pdf. Motion Picture
Association, Inc., Classification and Rating Rules (July 24, 2020), https://www.filmratings.com/downloads/rating_
rules.pdf. Responsible Marketing Pact, Content of Ads, https://the-rmp.eu/content/ (last visited Sept. 8, 2020).
[15] Android Developers, Application Fundamentals, https://developer.android.com/guide/components/
fundamentals(last visited Sept. 29, 2020) (“Android apps can be written using Kotlin, Java, and C++ languages.
The Android SDK tools compile your code along with any data and resource files into an APK, an Android
package, which is an archive file with an .apk sux. One APK file contains all the contents of an Android app and
is the file that Android-powered devices use to install the app.”).
[16] Gplaycli, https://github.com/matlink/gplaycli (last visited Sept. 8, 2020).
[17] We note that gaming apps constitute multiple genres, e.g. ‘Casual,’ ‘Educational,’ ‘Music’
[18] Brian Scott, The Dale-Chall 3,000 Word List for Readability Formulas, https://www.readabilityformulas.com/
articles/dale-chall-readability-word-list.php (last visited Sept. 9, 2020). See also Readability Formulas, The New
Dale-Chall Readability Formula, https://readabilityformulas.com/new-dale-chall-readability-formula.php (last
visited Sept. 9 2020).
[19] eMarketer, Mobile Trends 2020: 10 Trends to Monitor As 5G Ramps Up and Privacy Battles Loom, https://
on.emarketer.com/rs/867-SLG-901/images/Branch-Mobile%20Trends%202020%20Report%20Sponsorship.pdf
(last visited Sept. 16, 2020) (“…[T]here is a resurgence of interest in sophisticated contextual advertising, i.e., using
the context of the app or web page to infer the interests of the user instead of gathering historical behavior to
gauge those interests.”).
[20] See generally Digital Advertising Alliance, Consumer Assistance, WebChoices and AppChoices,https://
youradchoices.com/choices-faq#jr02 (last visited Sept. 16, 2020) (“When a user visits a website or uses an app
that works with an advertising network or other online advertising companies, these advertising companies gather
information about the user’s browser or device in order to tell when that same user browser or device visits other
websites or apps within the same network – even if these content oerings are run by dierent companies or have
dierent web addresses or brands. Over time, the information gathered about the browser or device may help
predict the user’s likely interest in particular categories of ads: for example, users who frequently visit baseball-
related websites might receive more ads for the “baseball/sports enthusiast” category, or users who engage with
automobile review apps might receive more ads for the particular models of cars that interest them. This inferred
interest category is used to provide advertising relevant to the category to a particularly browser or device.”).
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[21] Google, Make in-app purchases of Android apps, https://support.google.com/googleplay/
answer/1061913?hl=en (last visited Sept. 9, 2020) (“With some apps, you can buy additional content or services
within the app. We call these “in-app purchases.” Here are some examples of in-app purchases: A sword that gives
you more power in a game…A key that unlocks more features of a free app…Virtual currency that can be used for
purchases.”). Apple, Buy additional app features with in-app purchases and subscriptions, https://support.apple.
com/en-us/HT202023 (last visited Sept. 9, 2020).
[22] See generally Exodus Privacy, Who we are, https://exodus-privacy.eu.org/en/page/who/ (last visited Sept.
8, 2020). See also Exodus-Privacy, https://github.com/Exodus-Privacy/exodus-core (last visited Sept.8, 2020).
TAPP notes that the Exodus Core set of frameworks uses “dexdump,” a standard Android platform tool, to
disassemble the APK. This tool looks at the app manifest to determine permissions requested, then looks over the
bytecode for the signatures of known trackers previously identified by Exodus. Exodus Privacy, Trackers https://
reports.exodus-privacy.eu.org/en/trackers/ (Sept 8. 2020). See generally Google Git, Dexdump, https://android.
googlesource.com/platform/dalvik/+/09239e3/dexdump (last visited Sept. 8, 2020). Android Developers, App
Manifest Overview, https://developer.android.com/guide/topics/manifest/manifest-intro (last visited Sept. 8,
2020). Android Developers, Dalvik bytecode, https://source.android.com/devices/tech/dalvik/dalvik-bytecode
(last visited Sept. 8, 2020). See generally Wikipedia, Bytecode, https://en.wikipedia.org/wiki/Bytecode (last
visited Sept. 8, 2020) (“Bytecode, also termed portable code or p-code, is a form of instruction set designed for
ecient execution by a software interpreter. Unlike human-readable source code, bytecodes are compact numeric
codes, constants, and references (normally numeric addresses) that encode the result of compiler parsing and
performing semantic analysis of things like type, scope, and nesting depths of program objects.”).
[23] Exodus Privacy, What Exodus Privacy Does, https://exodus-privacy.eu.org/en/page/what/ (last visited Sept.
8, 2020) (“A tracker is a piece of software meant to collect data about you or what you do.”). Gunes Acar, Online
Tracking Technologies and Web Privacy (May 2017), https://www.esat.kuleuven.be/cosic/publications/thesis-289.
pdf (“Android apps and third-parties can access common identifiers present on the smartphone, such as MAC
address, Google Advertising ID or IMEI number.”). See generally Interactive Advertising Bureau, Mobile Identity
Guide for Marketers (June 2017), https://www.iab.com/wp-content/uploads/2017/06/Mobile-Identity-Guide-for-
Marketers-Report.pdf.
[24] See generally Digital Advertising Alliance, DAA Self-Regulatory Principles, https://digitaladvertisingalliance.
org/principles (last visited Sept. 14, 2020). See also OBA Principles Summary at 2, (“The Principles apply to online
behavioral advertising, defined as the collection of data online from a particular computer or device regarding
Web viewing behaviors over time and across non-aliate Web sites for the purpose of using such data to predict
user preferences or interests to deliver advertising to that computer or device based on the preferences or
interests inferred from such Web viewing behaviors.”) See generally Mobile Guidance. TAPP notes that the DAA’s
Self Regulatory Principles can be located here: https://digitaladvertisingalliance.org/principles.
[25] See generally Outfit7, Outfit7, https://outfit7.com (last visited Sept. 14, 2020).
[26] Android Developers, Permissions Overview, https://developer.android.com/guide/topics/permissions/
overview (last visited Sept. 16, 2020).
[27] Id.
[28] Android Developers, App Manifest Overview, https://developer.android.com/guide/topics/manifest/manifest-
intro (last visited Sept. 16, 2020) (“Every app project must have an AndroidManifest.xml file (with precisely that
name) at the root of the project source set. The manifest file describes essential information about your app to the
Android build tools, the Android operating system, and Google Play.”).
[29] We note that this discrepancy is likely the result of the way the Google Play Store tracks permissions.
Permissions requests listed in old versions of apps may still be listed on the Google Play Store, even if the current
version does not request those permissions. Consequently, data from the Google Play Store version will tend to
show that an app has a higher permissions requests count, versus the permissions requests count that results from
a static analysis of a downloaded APK.
[30] Android Developers, Permissions overview, https://developer.android.com/guide/topics/permissions/
overview (last visited Sept. 17, 2020). We do not analyze signature permissions requests as a distinct category in
this paper.
[31] Not all of the publicly scraped data for app store pages provided an app publisher location. Of the 1144 apps
in the teen dataset, all but 99 could be matched to their developer’s countries based on keyword matches to their
provided address. The 99 remaining either had blank addresses or provided so little information that they could
not readily be matched to a country. Of those 99, 87 could be matched with countries based on investigating the
developers to identify their likely locations. 12 apps remain unmatched.
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[32] Chinese Taipei and Hong Kong are included within “Greater China.” See generally Asia-Pacific Economic
Cooperation, https://www.apec.org/ (last visited Sept. 9, 2020).
[33] Here, we again used a threshold of countries with at least 30 app products to calculate which countries had a
set of apps with the highest percentage of in-app purchases.