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Angel or Devil? A Privacy Study of Mobile Parental Control Apps


Abstract and Figures

Android parental control applications are used by parents to monitor and limit their children’s mobile behaviour ( e.g., mobile apps usage, web browsing, calling, and texting). In order to offer this service, parental control apps require privileged access to system resources and access to sensitive data. This may significantly reduce the dangers associated with kids’ online activities, but it raises important privacy concerns. These concerns have so far been overlooked by organizations providing recommendations regarding the use of parental control applications to the public. We conduct the first in-depth study of the Android parental control app’s ecosystem from a privacy and regulatory point of view. We exhaustively study 46 apps from 43 developers which have a combined 20M installs in the Google Play Store. Using a combination of static and dynamic analysis we find that: these apps are on average more permissions-hungry than the top 150 apps in the Google Play Store, and tend to request more dangerous permissions with new releases; 11% of the apps transmit personal data in the clear; 34% of the apps gather and send personal information without appropriate consent; and 72% of the apps share data with third parties (including online advertising and analytics services) without mentioning their presence in their privacy policies. In summary, parental control applications lack transparency and lack compliance with regulatory requirements. This holds even for those applications recommended by European and other national security centers.
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Proceedings on Privacy Enhancing Technologies ; 2020 (2):314–335
Álvaro Feal*, Paolo Calciati, Narseo Vallina-Rodriguez, Carmela Troncoso, and Alessandra Gorla
Angel or Devil? A Privacy Study of Mobile
Parental Control Apps
Abstract: Android parental control applications are
used by parents to monitor and limit their children’s
mobile behaviour (e.g., mobile apps usage, web brows-
ing, calling, and texting). In order to offer this service,
parental control apps require privileged access to sys-
tem resources and access to sensitive data. This may
significantly reduce the dangers associated with kids’
online activities, but it raises important privacy con-
cerns. These concerns have so far been overlooked by
organizations providing recommendations regarding the
use of parental control applications to the public.
We conduct the first in-depth study of the Android
parental control app’s ecosystem from a privacy and
regulatory point of view. We exhaustively study 46 apps
from 43 developers which have a combined 20M installs
in the Google Play Store. Using a combination of static
and dynamic analysis we find that: these apps are on av-
erage more permissions-hungry than the top 150 apps
in the Google Play Store, and tend to request more dan-
gerous permissions with new releases; 11% of the apps
transmit personal data in the clear; 34% of the apps
gather and send personal information without appro-
priate consent; and 72% of the apps share data with
third parties (including online advertising and analyt-
ics services) without mentioning their presence in their
privacy policies. In summary, parental control applica-
tions lack transparency and lack compliance with reg-
ulatory requirements. This holds even for those appli-
cations recommended by European and other national
security centers.
Keywords: Parental control, Android, mobile apps,
static analysis, dynamic analysis
DOI 10.2478/popets-2020-0029
Received 2019-08-31; revised 2019-12-15; accepted 2019-12-16.
*Corresponding Author: Álvaro Feal: IMDEA Net-
works Institute / Universidad Carlos III de Madrid, E-mail:
Paolo Calciati: IMDEA Software Institute / Universidad
Politécnica de Madrid, E-mail:
Narseo Vallina-Rodriguez: IMDEA Networks Institute /
ICSI, E-mail:
1 Introduction
The dependency of society on mobile services to per-
form daily activities has drastically increased in the last
decade [81]. Children are no exception to this trend. Just
in the UK, 47% of children between 8 and 11 years of age
have their own tablet, and 35% own a smartphone [65].
Unfortunately, the Internet is home for a vast amount
of content potentially harmful for children, such as un-
censored sexual imagery [20], violent content [91], and
strong language [51], which is easily accessible to minors.
Furthermore, children may (unawarely) expose sensitive
data online that could eventually fall in the hands of
predators [2], or that could trigger conflicts with their
peers [30].
Aiming at safeguarding children’s digital life, some
parents turn to parental control applications to monitor
children’s activities and to control what they can do on
their smartphones [63, 78]. The typical parental control
app allows parents to filter, monitor, or restrict com-
munications, content, system features, and app’s execu-
tion [89]. Other apps provide parents with fine-grained
reports about children’s usage of the phone, their social
interactions, and their physical location. An example of
the former is the content discovery platform Safe Mode
with Free Games for Kids by KIDOZ, and of the latter
is the Norton Family app.
To provide these features, parental control apps rely
on the collection and handling of children’s behavioral
(e.g., location, browsing activities, and phone calls) and
personal data (e.g., unique identifiers and contacts), in
many cases, using techniques and methods similar to
those of spyware [27]. Yet, as with many regular An-
droid applications, parental control software may also
integrate data-hungry third-party advertising SDKs—
to monetize their software— and analytics SDKs—to
monitor the behavior of their users, create bug reports,
and build user profiles. As a consequence of Android’s
Carmela Troncoso: Spring Lab EPFL, E-mail:
Alessandra Gorla: IMDEA Software Institute, E-mail:
A Privacy Study of Mobile Parental Control Apps 315
permission model, these SDKs enjoy the same set of
permissions granted by the user to the host app. Apps
also might inadvertently expose data to in-path network
observers if they use insecure protocols to transmit sen-
sitive data. These practices involve great privacy risks
for minors, e.g., if data is used to profile children’s be-
havior or development, or if the data becomes compro-
mised [64].
To help parents choose among parental control so-
lutions, security centers at the European level [1, 33]
have analyzed a number of parental control solutions.
Their analysis considers four dimensions: functionality,
effectiveness, usability, and security—defined as their
effectiveness at deterring children from bypassing the
system. However, these reports do not provide any pri-
vacy risk analysis, nor consider the lack of transparency
or other potential violations of relevant privacy laws
with specific provisions to safeguard minors’ privacy,
e.g., the European General Data Protection Regulation
(GDPR) [29] and the U.S. Children Online Privacy and
Protection Act (COPPA) [35].
In this paper, we present the first comprehensive
privacy-oriented analysis of parental control apps for
Android from a technical and regulatory point of view.
We study 46 apps using both static and dynamic analy-
sis methods to characterize their runtime behavior and
their data collection and data sharing practices. We also
study the accuracy and completeness of their privacy
policies, identifying potential violations of existing reg-
ulations to protect minors. We note that during our
analysis we do not collect any data from children or any
other user. (§ 3.1.4 describes the ethical considerations).
Our main findings are the following:
A. Static taint analysis reveals that both apps and em-
bedded third-party libraries disseminate sensitive
information, e.g., the IMEI, location, or MAC ad-
dress (§ 4). Apps also use custom permissions to
obtain functionalities exposed by other apps’ de-
velopers or handset vendors, revealing (commercial)
partnerships between them.
B. We find that almost 75% of apps contain data-
driven third-party libraries for advertisement, so-
cial networks, and analytic services (§ 5). Further-
more, 67% apps share private data without user
consent (§ 6.3), even though some of these apps
are recommended by public bodies (e.g., SIP-Bench
III [1, 33]). Despite processing children’s data, 4% of
the apps use libraries which claim to not be directed
at children and thus do not take extra measures to
comply with privacy laws specific to children (e.g.,
U.S. COPPA rule [35]). Moreover, only two of the
seven ad-related libraries found are COPPA compli-
ant according to Google’s 2019 list of self-certified
libraries suitable for children apps [43].
C. The outgoing flows of 35% these apps are directed
to third parties, but 79% of the apps do not name
these organizations in their privacy policies (§ 6).
We find 67% apps collecting sensitive data without
user consent, and 6 apps that do not implement ba-
sic security mechanisms such as the use of encryp-
tion for Internet traffic.
D. Despite being required by regulations [29, 35], only
half of the apps clearly inform users about their
data collection and processing practices (§ 7). While
59% of the apps admit third party usage of sensitive
data, only 24% disclose the full list of third parties
embedded in the software. Furthermore, 18% do not
report any data sharing activity even though we find
evidence of third-party data collection through em-
bedded SDKs.
2 Parental Control Apps
Android parental control apps offer diverse features to
control children’s digital activities. In line with previous
work [56, 89], we classify apps that enable parents to
monitor children’s behavior, including location [47], as
monitoring tools; and we classify apps that enable par-
ents to filter content and to define usage rules to limit
the children’s actions as restriction apps. Some apps of-
fer multiple functionalities simultaneously, and we label
them as monitoring since it is the most invasive of the
two categories.
The way in which parental control apps enforce
these functionalities is varied. Common alternatives are
replacing Android’s Launcher with a custom overlay in
which only whitelisted apps are available; installing a
web browser where developers have full control to moni-
tor and restrict web traffic; or leveraging Android’s VPN
permission [13] to gain full visibility and control over
network traffic. The most common criteria to customize
app’s behavior are age and blacklisting/whitelisting.
The former restricts the type of content or defines the
level of monitoring depending on the children’s age. In
general, older children are granted access to a larger set
of webpages and applications than youngsters. In the
latter, parents can either list applications or webpages
that they deem inappropriate for their children, or add
appropriate content to whitelists.
A Privacy Study of Mobile Parental Control Apps 316
Most parental control apps have two different com-
ponents or modes: i)the parent app or mode, and ii)
the children app or mode. The parent app can run either
on the children’s or on the parent’s device enabling ac-
cess to the children’s app data, and to the dashboard for
setting monitoring or blocking rules. When the parent
mode runs on the children’s device, parents’ monitor-
ing can be done locally on this device. However, when
the parent and children app run on different devices,
the children app often uploads information to a central
server in order to enable remote access to the children’s
information to parents. Many apps in our study pro-
vide a web-based control panel in which parents can
access all information, and change both control and
blocking rules. This approach requires uploading data
to the cloud, and as a results, parents must trust service
providers to not disseminate nor process children’s data
for secondary purposes other than the ones declared in
the privacy policy.
To assist developers of children’s apps, Google has
made public best development practices [5], as well as
a list of self-certified third-party advertising and track-
ing libraries that respect children’s privacy [43]. While
parental control apps are not necessarily listed in the
Designed for Families (DFF) program [42], a Google
Play program for publishing applications targeting (and
suitable for) children, given their nature we expect them
to follow regulation’s special provisions and follow best
practices for collecting data about minors.
2.1 Regulatory Framework
Minors may be less concerned regarding the risks and
consequences of the dissemination of their personal data
to the Internet [29, 57]. This has motivated regulators
to develop strict privacy laws to protect children pri-
vacy such as the Children Online Privacy Protection
Act (COPPA) in the US. COPPA dictates that personal
data of children under the age of 13 can only be gath-
ered by online services, games, and mobile applications
after obtaining explicit and verifiable parental consent
[35]. In the EU, the General Data Protection Regulation
directive (GDPR) enforces privacy transparency and re-
quires data collectors to obtain consent from European
subjects prior to gathering personal data from them,
clearly stating the purpose for which it is collected [29].
As in the case of the USA COPPA rule, the GDPR
contains additional articles in relation to the privacy
of minors —namely Art. 8 [49] and Recital 38 [50] of
GDPR—which require organizations to obtain verifiable
consent from the parent or legal guardian of a minor un-
der the age of 16 before collecting and processing their
personal data. Both regulations explicitly prohibit the
collection and processing of minor’s data for the purpose
of marketing or user profiling, and force developers to
implement security best practices (e.g., use of encryp-
tion) and to disclose the presence and data collection
practices of embedded third-party libraries such as ad-
vertising and tracking services [29, 35].
3 Data Collection and Analysis
Google Play does not provide any specific category for
parental control apps. We identify them by searching
for the string “Parental control app” on Google Play’s
search engine. As this process can produce false posi-
tives, we manually eliminate any app that does not im-
plement parental control functionalities. We repeated
this process at different points during the project to
add newly published apps to our study. In total, we
found 61 parental control apps. Interestingly, the ma-
jority of these apps are classified as Tools (42% of apps)
by developers, despite the presence of the more specific
Parenting category (15% of apps).
Android app developers often release new versions
to include new features to their software or to patch
vulnerabilities [26]. Such changes may have an impact
on users’ privacy [73]. We crawl APKPure [14] —a pub-
lic dataset which contains historical versions of Android
applications—to obtain 429 old versions for the list of
61 apps so that we can study their evolution over time.
We discard versions prior to 2016, as we deem them
too old for our study. This also results in discarding 15
apps that have not been updated since 2016. Anecdo-
tally, one of the developers only made the parent ver-
sion available through Google Play, while its counter-
part was only available through direct download at the
developer’s website. We decided to download the chil-
dren version to avoid bias in our dynamic analysis. Our
final dataset contains 419 versions from 46 parental con-
trol apps (listed in Table 8 in the Appendix).
For each app we harvest metadata publicly available
on the Google Play store: app descriptions, number of
downloads, user ratings, privacy policies, and developer
details. We use this metadata in our analysis to contex-
tualize our results, showing potential differences across
A Privacy Study of Mobile Parental Control Apps 317
developer type and app popularity, and for analyzing
the completeness of privacy policies. 1
Apps’ popularity. The number of installs per app—
a metric often used to infer a given app’s popular-
ity [86, 92]—varies widely. The dataset contains 22%
of apps with over 1M downloads, whereas 37% of apps
have less than 100k downloads. When considered to-
gether, the 46 apps have been installed by more than
22M users (lower bound). The most downloaded apps
typically have a better user rating (on average 3.5 out
of 5 for apps over 1M downloads) than those with a
lower number of installs (on average 3.2 out of 5 for
apps below 100k installs).
App developers. We extract the apps’ signing certifi-
cates to identify the set of companies (or individuals)
behind parental control apps. We find that most devel-
opers release one single parental control app, except for
3 developers that have released two apps. 21% of the
developers also publish unrelated software in Google
Play besides the identified parental control apps. The
most remarkable cases are Yoguesh Dama [46], an In-
dian company which has published 38 apps (for volume
control, screen locking, wallpaper, and more), and Kid
Control Dev [45] a Russian company which also builds
apps such as flashlights.
One may assume that the use of parental control
apps is mostly predicated on trust. Therefore, we inves-
tigate whether the identity of the app developer plays a
role in the parents’ choice. Our initial hypothesis is that
parents might prefer software developed by well-known
security companies like McAfee or Norton. However, we
find that these developers only account for 9% of total
apps, and that they are not the most installed ones –
only one of them reaches 1M installs. The most popular
parental control apps are those developed by compa-
nies specializing in children-related software (e.g., Kid-
doware [54] and Screen Time Labs [77]). Most parental
control apps seem to monetize their software through
in-app purchases (from 1 EUR to 350 EUR) to unlock
features and monthly licenses, yet they typically offer a
free-trial period.
Delisted apps. We note that 10 applications were re-
moved from Google’s app store since our initial app col-
lection (Feb. 2018). The reason why these apps have
been delisted is unclear: their removal could be triggered
by the developers (e.g., companies no longer operating),
1The set of 46 apps may not contain every available parental
control app, but the apps are diverse from different standpoints
and can be considered as a representative the dataset.
Fig. 1. Analysis pipeline to assess the privacy risks and regulatory
compliance of parental control apps.
or by Google Play’s store sanitization process (Play Pro-
tect [7]). We still analyze these apps, and report when
they cannot be tested because they no longer work.
3.1 Analysis Pipeline
To carry out our analysis we take advantage of static
and dynamic analysis techniques previously developed
by the research community—in some cases extending
them to meet our research objectives—as shown in Fig-
ure 1. We use both static and dynamic analyses to over-
come the limitations they present when used in isola-
tion [72]. § 8 discusses the limitations of our method.
3.1.1 Static Analysis
For each app and version, we first parse its Android
Manifest file [11] to understand its high level behav-
ior without analyzing the binary code. Concretely, we
search for: i)apps with unusual permission requests,
and ii)apps that request a different number of per-
missions across versions. We complement this analysis
with static taint analysis to investigate potential per-
sonal data dissemination by apps and embedded third-
party SDKs. Finally, we look at the binary code to iden-
tify third-party libraries embedded in the app using Li-
bRadar [59]. This last step is critical to attribute ob-
served behaviors to the relevant stakeholder. We present
the results of our static analysis in § 4 and § 5. Static
analysis has no visibility into server-side logic, and can-
not analyze apps with obfuscated source-code. Further,
it may report false positives so we complement it with
dynamic analysis, as discussed next.
A Privacy Study of Mobile Parental Control Apps 318
3.1.2 Dynamic Analysis
The fact that apps declare a given permission or embed
a given third-party SDK does not mean that the permis-
sion will be used or that the library will be executed.
We use dynamic analysis to collect actual evidence of
personal data dissemination (§ 6.2), assess the security
practices of these apps when regarding network com-
munication (§ 6.4), and identify consent forms rendered
to the user at runtime (§ 6.3). We use the Lumen Pri-
vacy Monitor app [71], an advanced traffic analysis tool
for Android that leverages Android’s VPN permission
to capture and analyze network traffic – including en-
crypted flows – locally on the device and in user-space.
The use of Lumen only provides a lower bound to all net-
work communications in an app since it can only capture
flows triggered by user-, system- or environmental stim-
uli and codepaths at runtime. Achieving full coverage of
parental control apps’ actions is complicated and time
consuming: we must manually set up a parent account
and then exhaustively test the app by mimicking phone
usage by a child. The process of testing the applications
needs to be manual as the Android exerciser Monkey [8]
is not able to either fill login forms or test app features
in a non-random way [28]. We provide details on the
testing methodology in § 6.2.
3.1.3 Privacy Policy Analysis
We conduct a manual analysis of the latest version of
the apps’ privacy policies (§ 7) fetched from their Google
Play profile. We inspect them to identify: i)whether the
apps provide understandable and clear terms of use and
privacy policies to end users; ii)whether they are com-
pliant with the provisions of privacy regulations; and
iii)whether their disclosed policies match their behav-
ior in terms of access to and dissemination of sensitive
data and the presence of third-party libraries.
3.1.4 Ethical Considerations
Previous work has shown the use of parental control ap-
plications may have ethical implications [27, 61], there-
fore we do not collect any real data from children
or any other user. We perform our data collection on
fake accounts owned and operated by ourselves. Fur-
thermore all interaction with the apps is done by the
authors of this paper, without any children or end user
We put our focus on understanding the security and
privacy practices of these apps to determine to what
extent they could be a privacy threat to children. We
also stress that some of the privacy issues found in
mobile applications might be the result of bad coding
habits and lack of awareness—specially when integrat-
ing privacy-abusive third-party SDKs—, rather than
intentional developer misbehavior. We communicated
our findings to the Spanish National Data Protection
Agency (AEPD) and other government agencies pro-
moting Internet safety, namely INCIBE’s IS4K [1].
4 Permission Analysis
Android implements a permission model to control ap-
plication’s access to personal data (e.g., contacts and
email) and sensitive system resources (e.g., sensors like
GPS to access location) [10]. The analysis of the per-
missions requested by an app offers a high level idea of
what protected system resources and data the app has
access to. Besides Android’s official permission list de-
fined in the Android Open Source Project —grouped in
different risk levels, the most sensitive ones labeled as
“dangerous”—any app can define its own “custom per-
missions” [40, 83] to expose its resources and capabilities
to other apps. We study the use of Android permissions
and their evolution across releases to study what data
parental control apps access, how it changes across app
versions [26, 73], e.g., to adapt to stricter privacy re-
quirements at the platform level [12], whether this data
is potentially sent over the Internet, whether these per-
missions are used by third-party SDKs, the app itself,
or by both; and whether there are noticeable differences
between monitoring and restriction apps in terms of per-
missions request.
4.1 Prevalence and Usage of Permissions
Figure 2 shows the permissions requested by each one
of the apps in our dataset, as well as the category of
the permission. The top part of the plot shows restric-
tion apps and the bottom part lists monitoring apps.
Column-wise, we differentiate permissions according to
their privacy risk level [6]:
A. Dangerous permissions: (first block from the left,
in blue)–i.e., those that could potentially affect the
user’s privacy or the device’s normal operation, and
therefore should be explicitly granted by the user.
A Privacy Study of Mobile Parental Control Apps 319
com.bhanu .childlockla uncher
com.gizmoqui p.gamet ime
com.kiddoware .kidsafebr owser
com.kiddoware .kidsplace
com.kidslox.a pp
com.ootpapps. .lock
com.paren tsware.our pact.chil d
com.paren tycontrol
com.pdlp. android.a pp
com.redgre entree. app
com.whispe rarts.kid sshell
com.appobit .familyorb it
com.bitde fender.p arental 2013
com.calmea n.paren tal.control
com.cresoltech .trackidz
com.eset.p arental
com.fsp.and roid.c
com.kakatu.l auncher
com.kaspersky. safekids droid
com.locategy. family
com.mcafee.s ecurity.s afefamily
com.mmgua ildapp
com.mobilefe nce.famil y
com.mspy.l ite
com.nationa ledtech. Boomerang
com.qustodi o.qustodioap p d
com.saferkid. parenta pp
com.screent ime
com.securekid s.launch er_reloade d
com.sentry. kid
com.symant ec.familysa fety
com.teenl droid.chil d rentalB oardAgen t
funamo.fu namo
Fig. 2. Permission heatmap. We list one app per row, and we indicate the permissions it requests. We show restriction apps at the top,
and monitoring at the bottom. We differentiate dangerous permissions (blue), from signature (orange), and custom (yellow). Gradient
shows the percentage of releases requesting the permission, with darker meaning a higher number of releases.
Fig. 3. Comparison of percentage of apps asking for dangerous
permissions between Monitoring and Restriction apps.
B. Signature permissions: (second block, in orange) —
i.e., those that the system grants only if the request-
ing application is signed with the same certificate
as the application that declared the permission. We
also render system permissions in the same category,
even those that are deprecated in recent releases of
Android, but still work on older releases.
C. Custom permissions (last block, in yellow) —i.e.,
those that apps define to protect their resources.
Due to space constraints we do not display normal per-
missions —i.e., those which regulate access to data or
resources that pose little risk to the user’s privacy, and
are automatically granted by the system. However, they
are part of our study.
We use color gradients to show apps’ permissions
request changes over time in Figure 2. Darkest colors
A Privacy Study of Mobile Parental Control Apps 320
show that all releases of the app request the permis-
sion (i.e., no changes). Figure 2 shows that, as expected
due to their invasive nature, in general, parental control
apps need lots of permissions to offer their service. Con-
sidering the median value, parental control apps request
27 permissions, 9 of them being labeled as dangerous.
For comparison, the top 150 apps from the Google Play
Store in May 2019 request 18 permissions, 5 of them
labeled as dangerous. In fact, we find that the most ex-
treme parental control app in our dataset (Boomerang)
requests 91 permissions, of which 16 are dangerous;
much more that typically considered privacy invasive
non-parental control apps.2
We find that over 80% of parental control apps,
regardless of their category, request access to loca-
tion, contacts, and storage permissions. Popular non-
dangerous permissions requested by parental control
apps are related to Android’s package manager (pos-
sibly to monitor installed apps), and also to control sys-
tem settings. Despite the fact that we focus primarily
on dangerous permissions, it is important to highlight
that non-dangerous ones can be harmful too. One ex-
ample is the BIND_ACCESSIBILITY_SERVICE permission,
which is requested by 11 apps of the monitoring cate-
gory, and 1 of the restriction apps. This permission can
be used to monitor and control the UI feedback loop and
take over the device [39]. During the installation pro-
cess many of these apps explain that such permission
is necessary to better control the actions of the child.
Likewise, despite not being explicitly flagged as “dan-
gerous”, some of the deprecated system permissions are
also worth noting, since they protect low level function-
alities such as the ability to retrieve the list of running
processes on the device. Furthermore, the permission
model can be exploited to access personal data without
user consent via side-channels like using WiFi informa-
tion to get city level location or the MAC address in-
formation as a unique identifier [72].3While the type
of service provided by these apps might justify the use
of such dangerous permissions, the presence of third-
party libraries that can piggyback off these privileges to
gather children’s data is a privacy threat that we will
analyze in detail in § 5. 4
2For reference, Facebook requests 59 permissions, 16 of which
are dangerous.
3In fact, Android 10 introduced special permissions to avoid
the usage of the MAC address as a location surrogate and the
access to non resettable identifiers
4Because of Android’s permission model, third-party libraries
inherit the same set of permissions as the host application.
At the other side of the spectrum, we notice that
two parental control apps do not request any danger-
ous permission. One of them (Parental Control App by
Redgreentree) replaces the Android default launcher to
let the child use only apps approved by the parent.
The other app (SaferKid) is a parent app (which ex-
plains the lack of dangerous permissions) present in our
dataset because the developers only made the compan-
ion app and not the children version of the app available
in Google Play.
Anomalous permission requests. While most of the
apps requests dangerous permissions, we find that some
are rarely used by most parental control apps. We ana-
lyze in depth the permissions that are requested by only
10%, or less, of the apps in our dataset,—considering
monitoring and restriction apps separately—to identify
whether these permissions are justified. We also search
for any information on their privacy policy that may
justify their use. Table 1 provides a summary of the
anomalous permissions for which we found no justifi-
cation in the app’s description or privacy policy. Two
restriction apps request permissions to send SMS, pro-
cess outgoing calls and read contacts. Yet, we could only
find a justification for one of them in the app’s descrip-
tion. Regarding the monitoring category, we identify
seven apps requesting anomalous permissions (i.e., re-
ceive WAP push, record audio and read calendar). Three
of them are no longer indexed on the Google Play Store
(Kakatu,Family Orbit and Bitdefender). The remain-
ing four do not justify why they require access to these
permissions in their Google Play description or privacy
Permission requests across app releases. Previ-
ous studies show that Android apps tend to increase
the number of requested permissions across app re-
leases [25, 82, 87]. While 24% of the apps in our dataset
increased the number of permissions requested over
time, we find that another 24% of the apps decrease
the number of permissions requested over time, opposite
to the general trend. Part of this decrease is explained
by the fact that in early 2019 Google changed its per-
mission policy and disallowed apps to access SMS and
calls permissions unless they were the default messaging
app [44].
Restriction vs. Monitoring apps. Monitoring apps’
goal is to gather behavioral and usage data from chil-
dren’s phone and reporting it to their parents, either
via a dedicated web portal or in a parent-specific com-
panion app. Therefore, as Figure 2 reveals, monitoring
apps tend to request more dangerous permissions than
restriction apps. Figure 3 goes deeper into the specific
A Privacy Study of Mobile Parental Control Apps 321
Table 1. Unusual permission requests. We report whether the
apps have been unlisted from Google Play (GPlay column).
com.whisperarts.kidsshell READ_CONTACTS
com.appobit.familyorbit XREAD_CALENDAR
com.mmguardian.childapp RECORD_AUDIO
com.bitdefender.parental2013 XRECEIVE_WAP_PUSH
com.sentry.kid READ_CALENDAR
com.kakatu.launcher XRECORD_AUDIO
cases. Our empirical observations are aligned with our
expectations: compared to restriction apps, monitoring
apps commonly request permissions necessary to gather
children data such as geolocation, read call log or read
SMS. The difference between monitoring and restric-
tion apps is notable even for custom permissions (see
Figure 2), and in particular regarding browser control-
ling permissions. Monitoring apps access the browser
bookmark history more than restriction apps; however,
restriction apps tend to rely more heavily on custom-
specific browser permissions to control the browsing ac-
tivity on the child’s device.
Possible information dissemination. The high re-
quest rate of dangerous permissions does not imply that
parental control apps actually disseminate sensitive in-
formation over the network, either to their own servers
or to third parties. We apply static taint analysis to
estimate to what extent these apps access and dissem-
inate the sensitive information they access and with
whom. Using the context and flow sensitive analysis im-
plemented in Flowdroid [16], we observe that the apps
in our dataset include 1,034 method invocations to ac-
cess sensitive data when aggregated, never less that 78,
and on average 324. We also observe that 67% of apps
also have at least 1 sink—a part of the OS where data
can leave the system, such as the network interface—in
reachable code, suggesting potential information leaks.
Flowdroid reports at least one valid information flow in
14 of these apps, with the extreme case of Kids Zone,
for which it highlights 72 potential leaks. While it seems
that most of the reported information leaks involve log-
ging user actions (e.g., opening a new activity, logging
a failed/successful authentication, etc.), we also observe
more critical leaks involving sensitive data like unique
identifiers. Specifically, we find that two apps (Secure-
Teen and ShieldMyTeen) share the MAC address and
SSID through the Internet (they can be used as a side-
channel to uniquely identify the device and geolocate
the user [72]). The aforementioned apps and Dinner
Time Plus also disseminate the device IMEI, a unique
identifier; and four apps (MMGuardian,Shield My Teen,
GPS Tracker and Mobilefence) disseminate GPS-level
geolocation. We will further investigate in § 5.2 the ori-
gin and destination for these information leaks.
Custom Permissions. Android gives developers the
opportunity to define custom permissions in order to ex-
pose parts of the apps’ functionalities to other apps [83].
In the case of custom permissions the user never gets to
accept or reject them as they are automatically granted
without showing a warning or consent form to the user,
as opposed to Android official permissions. While we
did not render these permissions in Figure 2 for clar-
ity reasons, we include a plot showing the number and
type of custom permissions for each app in our dataset
in Figure 5 in the Appendix.
We find 28 custom permissions that—judging by
their name—have been declared by parental control app
developers. We find examples of custom permissions
from companion apps, e.g., the Spin browser [21], that
substitutes the default user browser, or the companion
parent app (e.g., com.safekiddo.parent). These permis-
sions are used from the children app to access or control
functionalities of the companion apps. We also find apps
using custom permissions declared by other developers,
such as the app Parents Around using custom permis-
sions from Protect your kid. This suggests the existence
of agreements between app developers to leverage func-
tionality implemented by other apps when both are in-
stalled on the same device. We also find several apps
using custom permissions related to phone manufactur-
ers, possibly enabled by pre-installed applications [40].
These vendor-declared permissions allow app develop-
ers to gain access to other system features not avail-
able through the official Android Open Source Project.
In some cases, as in the case of the Samsung KNOX
API, app developers must become Samsung partners to
gain access to their proprietary APIs [76]. Although the
parental control app Parents Around requires access to
custom permissions belonging to five manufacturers, in
general apps in our dataset tend to declare permissions
for either one vendor or none of them. The most frequent
custom permissions are related to Samsung and they
A Privacy Study of Mobile Parental Control Apps 322
are used by various releases in 21 apps of our dataset.
The most common vendor permissions are declared by
Huawei (10 apps), HTC (8 apps), and Sony and Oppo
(4 apps).
5 Third-party Library Analysis
Due to Android’s permission model, any SDK embed-
ded in an Android app inherits the privileges and per-
missions of the host app. This gives many organizations,
including third-party providers offering analytics or ad-
vertisement services, access to the same data as the
parental control app.
Many third-party library providers often prohibit
their usage in children-oriented software, such as
Branch [23] and Appboy (now rebranded as Braze) [24]
due to the strict regulatory frameworks implemented
in the USA and the EU to protect minors. Addition-
ally, Google released in May 2019 a list of third-party
libraries that self-verify being in compliance with the
provisions set by the GDPR and COPPA [43].
In this section, we inspect the parental control
apps—across app versions— in our dataset to find third-
party libraries and classify them by the functionality
provided using LibRadar [59]. We manually analyze its
output to sanitize the results and improve LibRadar’s
coverage, also mapping them to the actual organization
providing the service. 5After this process, we could suc-
cessfully identify 157 unique libraries (44 could not be
classified due to the use of code-obfuscation techniques).
Then, we use publicly available information to classify
each one of the libraries by its purpose, including their
own product descriptions and developer forums.
Table 2 shows for each resulting category the num-
ber of SDKs found, the total number of package names
matching each one of these libraries, and a short de-
scription of the category. Most third-party libraries em-
bedded in parental control apps are tools for develop-
ment support—general purpose development libraries,
JSON parsers, UI support libraries, etc—followed by
SDKs offering analytics services and advertisements —
e.g., Flurry and AppsFlyer. The latter category of SDKs
are more concerning from a privacy standpoint given
their data-driven behavior and business models. There-
5For example, LibRadar can report multiple package names
for the same library (e.g., com/paypal/.../onetouch and
com/paypal/.../data) so we cluster those belonging to the same
provider: PayPal.
Table 2. Classification, number and description of third-party
libraries found in parental control apps.
Category Lib # Description
Social Network 1 (60 pkgs) Social networks
Advertisement 7 (82 pkgs) Advertisement
Development 56 (582 pkgs) Development support tools
Functionality 29 (172 pkgs) App features (e.g., animations)
Support 43 (443 pkgs) Support libraries (e.g., DB
Analytics 21 (220 pkgs) Analytics services
Unrecognized 44 Unidentified libraries
Fig. 4. Percentage of apps using Advertisement, Analytics and
Social Network libraries.
fore, we focus our analysis on the libraries providing so-
cial network integration, online advertisement, and an-
alytics services.
Figure 4 shows that Google Ads, Google Firebase
and Google Analytics are present in over 50% of the
apps, followed by Facebook SDK at 43%. Some of these
libraries belong to the same parent company. For in-
stance, Crashlytics, Firebase, Google Analytics, and
Google Ads all belong to Alphabet [3]. Therefore, when
we group the SDKs by their parent company, we can
observe that Alphabet is present in 93% of the apps in
the dataset.
5.1 Apps Violating Libraries’ ToS
Finally, we study whether the third-party libraries em-
bedded in each parental control apps allow their use
in children-oriented software in their Terms of Ser-
vice. Reyes et al. showed that several apps designed for
children and published in Google Play’s Designed for
A Privacy Study of Mobile Parental Control Apps 323
Family program (DFF) used third-party libraries whose
Terms of Service (ToS) prohibit their integration in chil-
dren apps [74]. Following this insight, we use a static
analysis pipeline based on Soot and Flowdroid [16, 85]
to check whether parental control apps follow a similar
behavior. We note that some SDK providers changed
their ToS between our study and the previous work by
Reyes et al. We also study whether these libraries are
included in Google’s list of self-certified SDKs suitable
for children apps (as of May 2019) [43].
While both AppBoy and Branch still indicate that
they should not be included in applications directly tar-
geting children, our static analysis pipeline indicates
that they are present in children-oriented software as
parental control apps. We verify that two apps in our
dataset (GPS tracker and Qustodio) integrate these li-
braries in the latest version available in our dataset
(November 2018). Out of the seven advertising and an-
alytics libraries that we find in our dataset, only two
(AdMob and Unity) are present in Google’s list. We in-
clude the relevant wording extracted from their Terms
of Service in the Appendix.
5.2 Who Handles Sensitive Data and
What for
Android does not force developers to explain to users
whether a given permission is needed by the app’s func-
tionality and if it is also accessed by an embedded third-
party SDK. Therefore, users cannot make informed de-
cisions on whether to grant a given permission to the
application or not based on which software component
will gain access to it. To know who is responsible for
the data collection enabled by a given permission, we
statically analyze the bytecode of the most recent ver-
sion of each parental control app to search for Android
API calls protected by Android permission and then at-
tribute these calls to the host application’s code or to
an embedded third-party SDK. For that, we updated
the mapping between permissions and protected API
calls originally made by Au et al. to incorporate recent
changes in Android’s permission model [17, 19]. We rely
on LibRadar’s package identification to attribute the
protected API requests to the actual app, or to any em-
bedded third-party SDK.
Table 3 summarizes the results of this analysis. For
each API call protected by dangerous permissions, we
report whether it is used only in the application code,
only in the library code, or by both. We have an addi-
tional column reporting the percentage of uses for which
Table 3. Attribution of permission usage to code belonging to
application or third-party libraries for each dangerous permission.
The last column reports uses that we cannot attribute.
Permission %
& lib
CAMERA 14 7 7 72
GET_ACCOUNTS 25 12 4 59
READ_PHONE_STATE 39 26 18 17
READ_SMS 19 0 6 75
RECEIVE_MMS 0 25 0 75
RECORD_AUDIO 20 20 0 60
SEND_SMS 27 27 18 28
WRITE_CALL_LOG 12 12 0 76
we cannot attribute API calls to each software compo-
nent for reasons such as: i)the permission being re-
quested by the app developer in its Android manifest
file but not being invoked in the code; ii)our method
misses relevant API calls because of incomplete infor-
mation in Au’s mapping [17] 6; and iii)the use of code
obfuscation techniques. 7
A significant number of the calls to dangerous
permission-protected methods are invoked only by em-
bedded third-party libraries. For instance, embed-
ded SDKs access the fine location permission and the
read phone state permission, 8— 9% and 26% of the
times respectively—when the host app does not require
access to them. In other cases, SDKs piggyback on the
set of permissions already requested by the host app:
e.g., 55% of the times that fine location is requested
by the app, a third-party library also access the per-
mission. This suggests that users’ personal data might
6Despite our manual efforts to complement the mapping and
incorporate recent changes in the Android permission control
model, we cannot guarantee its completeness, since some map-
pings are not yet properly documented in the Android API.
7Seven instances of apps reading unique identifiers like the
MAC address, SSID, and IMEI were identified in obfuscated
code, thus they could not be attributed.
8A permission that allows reading unique device identifiers.
A Privacy Study of Mobile Parental Control Apps 324
be collected and processed for secondary usages beyond
those offered by the app.
On a per-app basis, the apps MMGuardian and
Mobilefence access users’ geolocation only in applica-
tion code but, for other apps, only in third-party code.
Specifically, we observe that the library com.proximity
—Google’s proximity beacon—is embedded in the app
ShieldMyTeen. This case might be justified for the need
to leverage geo-fencing methods. The app GPS Tracker
leaks the location in code that belongs to the library
Arity [15], which is a platform that collects location in-
formation to better understand and improve mobility.
While we find this library highly unusual for parental
control apps, we find that this apps is a “Family GPS
tracker” which can be used by parents as well. The de-
velopers are very explicit in their privacy policy about
sharing data with this company for driving features
present in the app.
In the following section we use dynamic analysis to
bypass some of the static analysis’ limitations, reporting
evidence of the collection and dissemination of children’s
personal data by these third-party libraries.
6 Dynamic analysis
The results of our static analysis show that some
parental control apps and embedded third-party SDKs
potentially collect and disseminate personal children
data to the Internet. In this section, we use dynamic
analysis methods to complement our static analysis and
collect actual evidence of personal data dissemination
to the Internet. To that end, we perform the following
user actions to execute and test each app —or pair of
apps if there is a parent or companion version—in our
A. We install the app and go through the setup pro-
cess. We create a parent account when necessary,
and grant consent to collect sensitive data when
asked. In order to perform an apples-to-apples com-
parison, when prompted, we configure each app to
monitor (or block) the activities of a child below 13
years of age. It is also important to note that we run
all tests from a European country (Spain) and that
when creating the account we are consenting to the
privacy policy and terms of services of the app.
B. Due to scalability issues, we cannot test the whole
spectrum of children actions that can be blocked or
monitored by a parental control app. Therefore, we
decided to focus on those that are more likely to be
blocked based on their permission requests or ad-
vertised features. Specifically, we manually interact
with the children device for five minutes as follows:
(i)we visit a newspaper in the browser (this action
is typically allowed for children), but also one porno-
graphic website, and a gambling service (this type
of traffic should be typically banned); (ii)we open a
permitted child game and a potentially blacklisted
dating app; (iii)we install and uninstall a game;
(iv)we take a picture with the default camera app
and (v)—while the test phone does not have a SIM
card—we go to the phone and SMS apps and at-
tempt to make a phone call and send a message.
Our analysis estimates a lower bound of all the possi-
ble instances of personal data dissemination that might
exist in our dataset of parental control apps when com-
pared to our static analysis. Also, since the Android
VPN API only allows one app to create a virtual inter-
face at a given time [9], our method does not allow us
to fully test 3 parental control apps that require access
to the VPN interface to monitor the children’s network
communications. We still report any data dissemination
that might happen prior to or during the VPN interface
being setup.
6.1 Third-party Services
Our results confirm the high presence of third-party
components in children-oriented apps as summarized
in Table 4. We observe network connections to a va-
riety of destinations in the recorded network flows. We
find 49 different second-level domains across all apps,
18 of which are associated with third-party Advertise-
ment and Tracking Services services according to the
list developed by Razaghpanah et al. [70]. The most
contacted domain is Crashlytics—now Firebase—which
is a Google-owned bug reporting and analytics ser-
vice contacted by 54.8% of apps. Every third-party do-
main found in our dynamic analysis experiments be-
longs to SDKs previously reported by our static anal-
ysis method. This also verifies some of our claims. For
instance, dynamic analysis confirms that the app GPS
Tracker contacts Branch services, an analytics platform
to increase revenues through “links built to acquire, en-
gage, and measure across all devices, channels, and plat-
forms” [22] that is not supposed to be used in children-
oriented software according to their own ToS.
A Privacy Study of Mobile Parental Control Apps 325
Table 4. Most popular third parties by apps contacting them
Third party Type # apps
Crashlytics Crash Reporting, Analytics 23
Facebook Graph Social Network, Ads, Analytics 15
Appsflyer Analytics 5
Adjust Ad Fraud, Marketing, Analytics 3
Google ads Advertising 3
OneSignal Push Notifications 3
Table 5. Data dissemination without consent
Data Dissemination Type Count (Unique SLD)
1st parties 3rd parties
Android Advertisement ID 2 8
Android ID 4 11
AP MAC address 1 0
WiFi SSID 2 0
IMEI 4 1
Geolocation 0 1
Email 2 0
6.2 Personal Data Collection and
Using Lumen we identify 513 flows containing personal
data (i.e., resettable and persistent unique identifiers,
geolocation, WiFi and Access Point information which
can be used as a proxy for geo-location, and list of pack-
ages installed) generated by 42 (91%) different apps. We
note that the access to the WiFi AP is a known side-
channel that has been used as a proxy to access users’
geo-location [72].
The fact that we see private data in network traffic
does not necessarily imply a privacy violation. This in-
formation might be uploaded to the cloud to deliver its
intended service, and to inform the companion app or
the parent directly through a web dashboard. In fact,
74% of these apps fall in the monitoring category, which
are those that request a higher set of permissions. Yet,
some types of data like unique IDs and location are ex-
tremely sensitive and should not be shared with third-
party services, particularly those offering advertising
and tracking services that do not comply with child pri-
vacy rules. We observe 4 apps disseminating the location
to a third-party domain in. Examples of third parties
collecting location information are analytics SDKs used
for client engagement and app growth (Amplitude), and
mobile push notification services (OneSignal). Other us-
ages might be for providing a service to the parent, as
in the case of mapping APIs (Google Maps). None of
these providers are in Google’s list of SDKs suitable for
children oriented apps [43].
Google encourages developers to use the Android
Advertisement ID (AAID) as the only user identifier [5].
The AAID is non-persistent and users can reset it or
opt out of “Ads personalization” in their device. The
combination of the AAID with other persistent iden-
tifiers without explicit consent from the user is also a
violation of Google’s Terms of Service [4]. Despite this,
we find 24 apps collecting and sharing persistent iden-
tifiers such as the IMEI, and 58% apps uploading the
AAID along with persistent identifiers, hence defeating
the purpose of resettable IDs. This behavior has been
previously reported for regular children apps published
in the DFF program [74]. Looking further into this issue
we find that half of the apps sending the AAID alongside
another unique identifier do so to a third-party library.
6.3 User Consent
Both COPPA and GDPR state that companies must
obtain verifiable parental consent before gathering data
from children below the age limit (13 years of age for
COPPA, 16 for GDPR) [29, 35]. App developers should
obtain verifiable parental (or legal tutor) consent before
collecting sensitive personal data from the minor using
techniques such as sending an email, answering a set of
questions, or providing an ID document or credit card
details at runtime [34]. This legal requirement implies
that informing parents or legal tutors about data col-
lection practices on the privacy policy is not enough,
especially if the app disseminates sensitive data to third-
party services as previously discussed.
In our previous analysis, we consented to data col-
lection in all our experiments by creating an account
and operating the app impersonating a child. Neverthe-
less, we want to determine whether apps collect private
data without parental consent. To do so, we rely on
the automatic method previously proposed by Reyes et
al. [74]: we launch each app and run it for five minutes
without interacting with it. This implies that we do not
actively consent to data collection and we do not carry
out any of the children actions, opting instead to leave
the app running with no input. As a result, any sen-
sitive or personal data—particularly unique identifiers
and geolocation—uploaded by the app to third parties
may be a potential violation of COPPA and GDPR.
A Privacy Study of Mobile Parental Control Apps 326
Table 6. Apps sending sensitive data without encryption
Application Data Type Destination
com.kidoz AAID
ru.kidcontrol.gpstracker IMEI & Location
com.parentycontrol Email
com.safekiddo.kid IMEI
com.kiddoware.kidsplace Android ID
com.kiddoware.kidsafebrowser Android ID &
Hardware ID
We find 67% apps disseminating personal data with-
out explicit and verifiable consent. The information
shared by these apps includes unique identifiers (i.e., the
android serial id, the AAID, or the IMEI) and the loca-
tion of the user (including alternative location methods
such as the SSID or the AP MAC address which have
been prosecuted by the U.S. FTC before [36, 72, 74]).
47% of all cases of non-consented data dissemination
are going to a third-party. Table 5 summarizes the data
types disseminated by the tested apps, grouped by the
type of data being disseminated. We note that none of
these libraries are in Google’s list of certified suitable
for children SDKs [43]. We believe that some of these
instances may correspond to developers being careless
in the way that they integrate third-party libraries, not
making sure that the user has read and accepted their
data collection methods before starting to collect data.
6.4 (Lack of ) Secure Communications
Both the COPPA [35] rule and the GDPR [29] have
clear provisions stating that developers must treat data
about children with an appropriate degree of secu-
rity [41, 48]. To assess that this is the case, we study
whether parental control apps make use of encryption
(e.g., TLS) to upload the data to the cloud. Table 6
summarizes the non-encrypted flows, sorted by pairs of
apps and domains, as well as the type of sensitive data
that is transmitted in the clear. We find instances of
persistent identifiers that enable tracking of users, e.g.,
the IMEI and the AAID, or geolocation information,
being sent in the clear. Finally, we observe one app (Se-
cure Kids) uploading without encryption the list of in-
stalled packages (used at the server to enable parents to
block unwanted apps) alongside an identifier-like string
(DEV_ID). The app com.kiddoware.kidsafebrowser ap-
pears in our results because it is installed as the default
web browser by one of the apps in the dataset.
7 Privacy Policy Analysis
Both GDPR and COPPA require app developers to
provide clear terms of use and privacy policies to the
end user. However, it is known that privacy policies
may be difficult to understand—even yielding differ-
ent interpretations—by average users [53, 67, 79, 88].
We manually inspect the privacy policies indexed by
Google Play Store for each app in our corpus to analyze
to what extent parental control app developers respect
the transparency obligations enforced by regulation. To
avoid biases introduced by ambiguous interpretations
of the policies, two authors conduct independent man-
ual analysis of each policy, and discuss with a third au-
thor in case of disagreement. We note that only 89% of
app developers had published a privacy policy on their
Google Play profile at the time of conducting our study
in February 2018. We discuss the results of our privacy
policy analysis along four axis, including examples of
wording found in the privacy policies as examples:
General considerations: We first look at whether the
policy can be found easily on Google Play, if it provides
clear information about the company (i.e., location of
company, main purpose, other apps), and if users are
notified upon policy changes. We find that 95% of apps
provide a direct link to their policies in their Google
Play profile, and 10% of apps provide information about
the companies. (e.g., The “Company” or “We/us”, with
domicile at [...]) However, despite the fact that changes
in privacy policies over time are hard to monitor by
users, only 20% of the apps state in their policies that
they actively notify users upon policy changes.
Data collection practices: We find that most privacy
policies (92.6%) report the kind of data being collected
from minors. However, only a few of them (54%) clearly
inform users of how these data are processed and for
what purpose (e.g., Your account and contact data are
used for managing our relationship with you). We also
see that only roughly half (56%) of the policies portray
how long collected data will be stored at the developer’s
Data sharing practices: Our static and dynamic anal-
ysis show that many parental control mobile applica-
tions rely on third-party libraries offering crash report-
ing, analytics, or advertising services. When any em-
bedded third-party service collects or processes personal
data from users, both GDPR and COPPA require de-
velopers to list them on the privacy policy of the app.
We find that 59% of apps talk about third-party service
usage in their privacy policy, but only 24% of the apps
A Privacy Study of Mobile Parental Control Apps 327
list the type of data being sent to them. Finally, com-
panies can also share or sell data to other companies
(e.g., data brokers). Even though we see that 78% of
policies talk about the possibility of sharing customer’s
data, they say that this will only happen as a result of
company acquisition or legal compulsion.
Third-party service disclosure: We verify whether
apps are transparent when they refer to third-party ser-
vice usage in their policies by cross-checking these poli-
cies with our empirical findings regarding the usage of
third-party libraries and data dissemination (§ 6). Since
GDPR introduced the need to name the third-party
companies receiving personal data, we check this in the
25 apps for which we have privacy policies collected from
after the GDPR became effective on the 25th of May of
2018. Table 7 shows the number of apps using a third-
party service and how many of them actually report
this in their privacy policy (e.g., Google may use the
Data collected to contextualize and personalize the ads of
its own advertising network). Only 28.0% name all the
third-party services that we find during runtime, while
79% of the studied apps do not name any third par-
ties that they share private data with. We note that the
latter apps are potentially in violation of both COPPA
and GDPR for not being clear about their data shar-
ing partners. Furthermore, we find one app that shares
location data with a third-party service, which can con-
stitute a potential violation of the FTC COPPA rule
which prohibits the collection of children’s geolocation
sufficient to identify a street name and city or town.
While this app is not directed only at children, its com-
pany name is Family Safe Productions, hinting that it
can be used for monitoring child locations. Nevertheless,
they openly say in their policy that they can share lo-
cation data with third parties (as reported in § 5.2). We
note that we did not find this behavior in our dynamic
analysis experiments.
Regulatory compliance: We also study developers’
awareness about different related regulation. Only 22%
of apps claim COPPA compliance in their policy; and,
while only 10% of policies talk about European legisla-
tion, 37% mention their compliance with local laws (e.g.,
Our Privacy Policy and our privacy practices adhere to
the United States Children’s Online Privacy Protection
Act (“COPPA”), as well as other applicable laws).
User privacy rights: Finally, we check the rights and
choices that users have about their data. To do so we
look at the number of apps that allow users to opt out
of the data collection practices without having to cease
the use of the app, and how many apps give the user a
chance to correct, delete, or access their data. We find
Table 7. Third party presence in apps and on their privacy policy
Third party Type # apps # apps listing
them in policy
Crashlytics Crash Reporting, An-
23 5
Social Network, Ad-
vertising, Analytics
15 3
Appsflyer Analytics 5 1
Adjust Ad Fraud, Market-
ing, Analytics
3 0
Google ads Advertising 3 1
OneSignal Push Notifications 3 1
Amplitude Analytics 2 0
Help Shift Customer service 1 0
Apptentive Analytics, User En-
1 0
Branch Analytics 1 0
Splunk Analytics 1 0
that in 46% of apps users can opt out of data collection,
where 63% of apps give users at least one of the above
choices (e.g., According to European Union applicable
data protection legislation (GDPR), data subjects shall
have the right to access to data, rectification, erasure,
restriction on processing [...]).
7.1 The GDPR Effect
We first analyzed the privacy policies at the beginning
of our study, around three months before the GDPR
became effective in May 2018. While we do not revisit
the whole analysis, we use the results from our study to
evaluate the evolution of the privacy policies for the 25
apps that share data with third parties and that are still
available after the 25th of May 2018. Our goal is to learn
if companies changed their policies, to understand the
impact of the law in the specific case of parental control
apps. Comparing the newer policies with the ones of our
previous analysis, we find that 2 have major changes in
their privacy policy after GDPR. Looking more in de-
tail into the specific cases we find that one app initially
did not name its data sharing partners in the privacy
policy, and later added that info. Another app did not
explain clearly the type of data being collected, and has
now changed its policy to be clearer. However, we see
that most policies are still unclear as they often omit
names of third-party services used by the application.
Thus, even when regulatory frameworks push to shed
light into the third-party mobile ecosystem, we see the
A Privacy Study of Mobile Parental Control Apps 328
necessity for external auditing of mobile parental control
applications beyond usability, and capability aspects.
8 Discussion and Limitations
Our multilateral analysis of parental control apps brings
to light a large number of undesirable behaviors regard-
ing children’s privacy. First, parental control applica-
tions request a large number of invasive permissions.
This, combined with the number of analytics and adver-
tisement SDKs embedded in these apps pose a serious
threat for children. Not only do these libraries piggyback
on the large number of sensitive permissions requested
by parental control apps to collect personal data; but
there are a number of requested permissions used solely
by third-party components. We also find empirical evi-
dence of data sharing practices with third parties: 72%
of the apps share data with a third-party SDK, and
in 67% apps this sharing happens without explicit and
verifiable parental consent. In some cases we observe
that the upload of sensitive data to online servers hap-
pens without encryption. Finally, our analysis of the
apps’ privacy policies reveals that they are far from clear
about their data collection practices, and usually under-
report on what data is shared with other services. Such
practices put in question the regulatory compliance of
many of these apps.
Given the severe privacy risks revealed by our anal-
ysis, we consider that privacy implications should be a
fundamental part of any guide designed for parents. We
compare our findings with existing recommendations by
public bodies to help parents decide whether to use this
type of software and which tool is more secure. Our
dataset contains 5 of the 10 apps benchmarked in the
SIP benchmark [33] by the European commission, which
focuses on usability and resistance to children sidestep-
ping attempts. Two of these apps follow privacy-risky
practices: Qustodio (also mentioned on a large amount
of online studies that recommend top parental control
apps [68, 75]) shares data with third-parties without
consent, and Parentsaround does so in addition of shar-
ing unique identifiers with third-party services. We also
compare our results to IS4K Cybersecurity, which lists a
series of apps, enumerating their functionalities without
any judgment on their suitability. Our dataset includes
6 out of the 10 apps in their list. Three of these apps
share sensitive data with third parties without appropri-
ate consent, and another app sends data unencrypted.
Furthermore, we argue that app stores should take
extra measures for verifying that applications directed
at children comply with current legislation and treat
children data with extreme care even if they are not
in the designed for families program. While a com-
plete and exhaustive analysis of apps could be unfea-
sible, static analysis showing the presence of analytics
and advertisement libraries in apps that will be used by
minors—including SDKs that prohibit their use in chil-
dren apps—should raise concerns. Furthermore, simple
dynamic analysis to verify that these apps use the most
basic security measures, such as the use of encryption,
would already help reducing the risks for children pri-
Comparison to apps of different nature. In this
work we show that parental control applications often
misbehave posing privacy threats for children and even
parents. However, we cannot say that they are worse
in behavior than other Android apps, even children-
oriented apps studied in the literature [70, 74]. Previous
studies show that security and privacy misbehavior is a
common trend in Android applications: they often re-
quest more permissions than needed [37], and include a
large number of third-party libraries [59, 70]. Regarding
the privacy risks of children-oriented applications pub-
lished in Google Play’s Designed for Families program,
Irwin et al. showed that almost 50% of the apps were in
potential violation of the COPPA rule [74].
Much like in the case of children-oriented apps
present in Google’s DFF program, parental control apps
should comply with children-oriented regulation and
special provisions. By definition, they are directed at
children. However, none of the apps analyzed in this
study is listed on Google Play’s DFF program. While
previous work has shown that Android app’s tend to
collect plenty of user data [66, 70], in some cases even
avoiding the security mechanisms implemented by the
platform [62, 72], the fact that parental control apps po-
tentially do not comply with specific regulation is much
more worrisome – both for parents and minors.
In fact, there is a big element of trust from par-
ents towards parental control solution developers, which
is broken when these services treat children data care-
lessly, share it with third-parties, or send it across the
Internet unencrypted. This misbehavior and parents’ in-
ability to audit mobile applications calls for action from
researchers and data protection agencies to understand
the way in which these apps might be violating current
legislation and invoke regulatory actions to fix these is-
sues. Until that moment, some studies proposed the use
of non-technical solutions for parental control [58].
A Privacy Study of Mobile Parental Control Apps 329
Limitations. Our app collection method relies on us-
ing the free search capabilities of the Google Play Store,
which means that we did not explore every parental con-
trol app on the market. Nevertheless, we believe that the
set of apps chosen is representative in popularity, devel-
oper characteristics, and behavior. Also, as our initial
data collection started two years prior to submission,
some apps were removed from the marketplace during
our analysis and new apps had appeared. We repeated
the data collection pipeline later on our study to find
newly published apps, and we downloaded historical
version of every app that we analyzed to always cover
the same time-frame.
Despite combining static and dynamic analysis, we
acknowledge that our analysis cannot guarantee full cov-
erage of the code, app features, or the data flows. First,
the static analysis will miss code paths implemented
in native or obfuscated code. Second, LibRadar uses
a database to identify third-party libraries and cannot
detect libraries that are not present in this database.
Third, Lumen can miss flows that are not triggered by
our pre-defined actions. Furthermore, we perform our
dynamic analysis on the children app, and it may be
that the companion parent app disseminates sensitive
data over the network,. Additionally, our Man-in-the-
middle proxy is unable to intercept TLS flows for 2
apps that might use techniques like TLS certificate-
pinning [69]. Finally, our dynamic analysis has been exe-
cuted from a testbed geolocated in the European Union.
It would be necessary to verify our claims in the U.S. to
further investigate potential COPPA violations.
9 Related Work
Researchers resort to static or dynamic analysis –or a
combination of both, as in our study– to analyze the be-
havior of mobile apps. Static analysis techniques have
been used to identify possible information leaks in An-
droid [16, 55], to extract behavioral features for mal-
ware detection [18, 38], or to study the evolution of
the Android ecosystem [25, 26, 72, 82, 87]. Similarly,
many dynamic analysis techniques have been used for
the same or similar purposes [32, 52, 66, 71, 80], de-
spite the challenges to scale and automate the analysis
to large datasets [28, 74]. While we rely on similar static
and dynamic techniques for our study, the final goal is
quite different, as none of these works focuses on the
privacy aspects of parental control apps.
We build on top of previous work on the domain
of parental control applications and children privacy.
There have been studies on the effectiveness of parental
control solutions: Mathiesen argued that such policies
are a violation of children’s right to privacy [61] while
Wisniewski et al. presented a comprehensive study of
the features offered by parental control apps and con-
cluded that privacy invasive monitoring features are
more common than those focusing on teen self regu-
lation [89]. Furthermore, Eastin et al. showed that par-
enting style has an effect on the type of restrictions that
parents set on their children’s phone usage [31].
We believe this to be the first study taking a look at
the privacy implications of parental control apps from
a technological point of view. Others have studied pri-
vacy implications for children in domains such as social
media [60] and smart toys [84, 90]. Reyes et al. [74] is
the closest work to ours from a methodology standpoint.
They analyze mobile apps’ compliance with the COPPA
regulation. While some of their findings are similar to
what we present in this paper, our work is a more thor-
ough analysis of the parental control mobile app ecosys-
tem. Chatterjee et al. also studied the parental control
ecosystem. However, they focus on assessing how such
applications may be used for other purposes (e.g., spy-
ware and partner violence) [27].
10 Conclusion
We have presented the first multi-dimensional study of
the parental control apps ecosystem from a privacy per-
spective. Our findings open a debate about the privacy
risks introduced by these apps. Does the potential of
parental control apps for protecting children justify the
risks regarding the collection and processing of their
It is our hope that our study raises awareness in
parents and regulatory authorities on the risks brought
up by some of these applications. We stress that it is
fundamental to complement current benchmarking ini-
tiatives [1, 33] with a security and privacy analysis to
help parents to choose the best application while taking
these aspects into consideration.
The authors would like to thank our anonymous review-
ers and our shepherd Prof. Micah Sherr (Georgetown
A Privacy Study of Mobile Parental Control Apps 330
University) for their valuable feedback and their help
for preparing the final version of this paper. The au-
thors also thank Julien Gamba for his help in the man-
ual inspection of privacy policies and Juan Caballero
for his contribution on the initial version of this work.
This project is partially funded by the US National
Science Foundation (grant CNS-1564329), the Euro-
pean Union’s Horizon 2020 Innovation Action program
(grant Agreement No. 786741, SMOOTH Project), the
Spanish national projects DEDETIS (TIN2015-70713-
R) and SCUM (RTI2018-102043-B-I00), the Spanish
grant TIN2017-88749-R (DiscoEdge) and by the Madrid
regional project BLOQUES (S2018/TCS-4339).
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A Privacy Study of Mobile Parental Control Apps 333
Fig. 5. Number and type of custom permissions found in the apps in our dataset
3rd-party Library ToS
Below, we list the Terms of Service for the third-party li-
braries prohibiting their usage on children-oriented soft-
ware: [23]:
You will not use our Services to: {...}(ix) create lists
or segments of children under the age of 13 (and in cer-
tain jurisdictions under the age of 16), advertise mo-
bile Apps that are directed to children under 13 (and in
certain jurisdictions under 16), and/or knowingly mar-
ket products or services to children under 13 (and in
certain jurisdictions under the age of 16), without em-
ploying appropriate settings within the Branch SDKs to
limit data collection for children under 13 (and in cer-
tain jurisdictions under 16), in order to comply with
any applicable laws protecting children (including, but
not limited to, GDPR and the U.S. Children’s Online
Privacy Protection Act ("COPPA");
Appboy [24]: (now branded as Braze)
Our Services are not directed to individuals under the
age of 13. We do not knowingly collect personal infor-
mation from such individuals without parental consent
and require our Customers to fully comply with applica-
ble law in the data collected from children under the age
of 13.
Custom Permissions
Figure 5 dives deeper on the number and type of custom
permissions for every app in our dataset.
Permission Heatmap
Figure 6 shows a landscape version of Figure 2 to im-
prove readability.
List of Analyzed Apps
Table 8 provides a classification of each app attending to
whether it is a monitoring or restriction app, if it is cur-
rently available on Google Play and if it is benchmarked
by the SIP and IS4K alternatives.
A Privacy Study of Mobile Parental Control Apps 334
com.bha nu.chil dlocklaun cher
com.gizmoqu ip.gam etim e
com.kiddowa re.kidsa febrowser
com.kiddowa re.kidsp lace
com.kidslox. app
com.ootpapp app.l ock
com.pare ntswar e.ourpa ct.chil d
com.pare ntycontr ol
com.pdlp .andr
com.redg reent ree.ap p
com.whisp erart s.kidssh ell
com.appob it.fami lyorbit
com.bitd efende r.pare ntal2 013
com.calme rental .control
com.cresolt ackidz
com.eset .paren tal droid.c
com.kakatu. launch er
com.kasper sky.safekids
com.kidgy.a ndroid
com.locateg y.famil y
com.mcafee .securi ty.safefa mily
com.mmg uardia n.chil dapp
com.mobil efence.fa mily
com.mspy. lite
com.nati onaledt ech.Boome rang
com.qust odio.qust odioapp
com.safekid do.kid
com.saferkid .pare ntapp
com.screen time
com.secur uncher _reloade d
com.sent ry.kid
com.syman tec.fam ilysafet y
com.teen limit .androi d.child
com.vionika.p arent alBoar dAgent
con.netsp le
funam o.funam o
Fig. 6. Landscape version of the permission heatmap shown in Figure 2
A Privacy Study of Mobile Parental Control Apps 335
Table 8. Summary of our corpus of apps attending to different features. The column “benchmarked” indicates whether the app has
been analyzed by [1, 33]. The values “M” and “R” stand for Monitoring and Restriction, respectively
Name Type Currently listed (2019/05) Benchmarked
com.mmguardian.childapp M X
com.infoweise.parentalcontrol.secureteen.child M X
com.qustodio.qustodioapp M X X
com.kaspersky.safekids M X
com.kiddoware.kidsafebrowser R X
com.securekids.launcher_reloaded M X X
com.eset.parental M X X M X M X
com.nationaledtech.Boomerang M X M X
com.infoweise.parentalcontrol.shieldmyteen M M X
com.bhanu.childlocklauncher R X
com.safekiddo.kid M X
com.kidoz R X
com.cresoltech.trackidz M
net.safelagoon.lagoon2 M X
com.screentime M X X
com.kiddoware.kidsplace R X M X
io.familytime.parentalcontrol M X X
com.vionika.parentalBoardAgent M
de.protectyourkid M X
com.parentsware.ourpact.child R X
ru.kidcontrol.gpstracker M X R X
com.zerodesktop.appdetox.dinnertimeplus R X R X X R
com.sentry.kid M X
com.whisperarts.kidsshell R X
funamo.funamo M X
com.bitdefender.parental2013 M
com.gizmoquip.gametime R
com.kakatu.launcher M
com.calmean.parental.control M X M X X
com.saferkid.parentapp M X M X M X
com.parentycontrol R
com.leapteen.parent M M
com.mspy.lite M X
com.appobit.familyorbit M
... Concerning parental control apps, A. Feal et al. [22] conducted an in-depth study of the Android parental Vulnerable key generation protocol Passkey is vulnerable to brute force attacks [16] [17]. All ...
... This study distinguishes between monitoring apps, which enable parents to monitor children's behaviour (including location), and restriction apps, which enable parents to filter content and define usage rules to limit the children's actions. Regarding the use of permissions, A. Feal et al. [22] showed that parental control apps request 27 permissions on average, 9 of them being labelled as dangerous. These dangerous permissions were used to leak data to remote servers in many cases. ...
... Most of these data leaks required logging user actions (e.g., logging a failed/successful authentication), and some involved sensitive data like unique identifiers, such as the device's IMEI. Some apps analysed in [22] also use custom permissions to obtain functionalities exposed by other developers or handset vendors, revealing (commercial) partnerships between them. Many of the calls to dangerous permission-protected methods were invoked only by embedded third-party libraries. ...
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The increased use of wearables in recent years has fostered great technological development in this area, although without the appropriate supervision, usability may go first over security. In addition to this, the fact that wearables have been requiring more and more personal data from the user makes them attractive devices for an attacker. In this paper, we propose a set of tests for evaluating the security and privacy of wearables, and we apply them to analyse the security and privacy of a set of commercial wearables that are targeted at minors, who represent a group with especially high requirements in this regard. We define the testing scenario, expose the tools to support the research, and specify the testing process to be followed. Based on the obtained results, although the considered low-end devices are broadly speaking less secure than high-end ones, most of them present security and privacy flaws, which illustrates the necessity of regulation that ensures the fulfilment of appropriate security and privacy requirements.
... Moreover, the research on parenting apps reveals significant risks around privacy, data security and the spread of misinformation. Parents are increasingly relying on apps to track their child's development, but many apps fail to protect users' sensitive data or provide evidence-based information [32,33]. Some apps provide contradictory or misleading advice on controversial topics without citing medical sources [34]. ...
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Background: Women are increasingly using breastfeeding apps to facilitate and organize breastfeeding; however, no study has so far focused on maternal psychopathological risk and on the quality of dyadic exchanges in this field. Aim and methods: This preliminary, descriptive study aimed at evaluating levels of psychopathological symptoms (through the SCL-90/R) and for the quality of the interactions they have with their children during feeding (through the SVIA) in mothers who use breastfeeding apps with different grades of engagement. Results: Data analyses showed that mothers with a mild use of the apps present a higher quality of dyadic interactions during feeding and lower psychopathological risk. Conclusions: The cross-sectional and descriptive nature of this study does not allow any causal conclusions. However, results suggest that the higher the engagement and use of breastfeeding apps, the lower the quality of feeding interactions and the higher the maternal psychopathological risk.
... Basically, parental control applications are used by parents to monitor and limit their children's mobile behaviour such as apps usage, web browsing, calling, and texting. In order to offer this service, parental control apps require privileged access to system resources and access to sensitive data [3]. ...
Conference Paper
Bullying, skipping classes and missing child case are still very prevalent in Malaysia even the police have involved and took some action. For those children who go to school or playground without parent accompany, there is a risk for them went missing, being bullied and molested outdoor either intentionally or unintentionally. There is a need for the parent to monitor their child and even know their child’s location when they are outdoor. Thus, the ideas of the proposed system are to help the parent to monitor and keep track of their child’s location when the child is prone to danger. This paper discussed about development of android-based application named Parent’s-Closed-Circuit Mobile TV (CCMTV). This application was developed with the intention to help the parent to monitor their children since many of today’s parent was busy and have little supervision on their children. The android parental spying app is a monitoring app that can view and listens to children’s surrounding and tracks their GPS location. This system requires both (child and parent) applications to be operated under a stable internet connection. It is an Android development project that use the access permission of the android devices to work as a spying app which are dependent on the Google API and at the same time does not require device rooting. A study on the similar monitoring apps based on the top five parental control apps in 2019 has been made to find limitations to be enhanced. The development of the app is using the client-server architecture model where the client is both the parent and the child while the server is using the Firebase server. This system also uses the media server for its live streaming capabilities. The system development methodology chosen for this system is structured evolutionary prototyping with increment method and the only hardware needed for this project is the android smartphones and a laptop for development. The development of this application was done with Android Studio platform. This system only limits parent on a passive and stealth monitoring but not to apply restrictive control on their kid’s smartphone usages.
... proposed a system to detect their malicious behaviors at runtime, which when used on child-directed apps uncovered that most prompted kids to disclose their personal data to advertisers [39]. Researchers who tested apps targeting wider audiences (e.g., parental control apps [44] and smart TV apps [57]) similarly identified behaviors that threaten user privacy and make the apps potentially non-compliant with applicable regulations. ...
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We investigate the privacy compliance processes followed by developers of child-directed mobile apps. While children’s online privacy laws have existed for decades in the US, prior research found relatively low rates of compliance. Yet, little is known about how compliance issues come to exist and how compliance processes can be improved to address them. Our results, based on surveys (n = 127) and interviews (n = 27), suggest that most developers rely on app markets to identify privacy issues, they lack complete understandings of the third-party SDKs they integrate, and they find it challenging to ensure that these SDKs are kept upto-date and privacy-related options are configured correctly. As a result, we find that well-resourced app developers outsource most compliance decisions to auditing services, and that smaller developers follow “best-effort” models, by assuming that their apps are compliant so long as they have not been rejected by app markets. We highlight the need for usable tools that help developers identify and fix mobile app privacy issues.
... Shipp and Blasco [70] analyzed 30 women's reproductive-health apps and uncovered that privacy policies often neglect to disclose reproductivehealth-related data, though the apps collect such data and transfer them to remote servers. Feal et al. [71] studied 46 parental control Android apps and found that 72% of the apps shared personal data with third parties without disclosing this sharing practice in privacy policies. Onik et al. [72] found that app publishers and associated owners could track end-users by aggregating private data such as contact number, biometric ID, email, location, and social graph. ...
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Privacy laws and app stores (e.g., Google Play Store) require mobile apps to have transparent privacy policies to disclose sensitive actions and data collection, such as accessing the phonebook, camera, storage, GPS, and microphone. However, many mobile apps do not accurately disclose their sensitive data access that requires sensitive ('dangerous') permissions. Thus, analyzing discrepancies between apps’ permissions and privacy policies facilitates the identification of compliance issues upon which privacy regulators and marketplace operators can act. This paper proposes PermPress – an automated machine-learning system to evaluate an Android app’s permission-completeness, i.e., whether its privacy policy matches its dangerous permissions. PermPress combines machine learning techniques with human annotation of privacy policies to establish whether app policies contain permission-relevant information. PermPress leverages MPP-270, an annotated policy corpus, for establishing a gold standard dataset of permission completeness. This corpus shows that only 31% of apps disclose all dangerous permissions in privacy policies. By leveraging the annotated dataset and machine learning techniques, PermPress achieves an AUC score of 0.92 in predicting the permission-completeness of apps. A large-scale evaluation of 164, 156 Android apps shows that, on average, 7% of apps do not disclose more than half of their declared dangerous permissions in privacy policies, whereas 60% of apps omit to disclose at least one dangerous permission-related data collection in privacy policies. This paper’s investigation uncovers the non-transparent state of app privacy policies and highlights the need to standardize app privacy policies’ compliance and completeness checking process.
The home is a crucial site of young children's early encounters with digitally connected technologies. It is here that their emerging digital footprints are being formed and where digital data about them is being produced then collected, analysed and commodified in varying ways. While much is speculated about the rise of intelligent assistants, baby monitors, connected toys and goods, there is little quantitative information available about what sorts of devices households with children actually contain. This article reports on findings from an online survey of 504 Australian households with children aged 0–8 years. The survey was designed to capture a snapshot of internet connected devices and goods in households as a way of contextualising current discussions around the datafication of childhood. Results indicate that Australian households with young children are indeed highly connected, and this is primarily via devices already well domesticated into everyday family life such as TVs, computers and smartphones. We discuss several key points emerging from our findings, including: the safety and security of the household as a primary motivator for using smart home devices; the different rates of acceptance of the datafying objects in the home; and the Googlization of family life. We conclude the paper by outlining a research agenda that more accurately reflects the digital realities of Australian family life.
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En la actualidad, está en auge del uso de dispositivos que se pueden llevar puestos (wearable devices, en inglés): tanto niños como adultos han incorporado en su día a día este tipo de dispositivos adicionales al celular, que hoy son una extensión más del ser humano. Esta investigación hace una revisión de qué son los wearables, su uso y los riesgos que implican para el usuario en cuanto a su privacidad y su seguridad. Se eligió la metodología de identificación y control de riesgos entre los resultados de una revisión de alcance en la literatura pertinente.
News headlines about privacy invasions, discrimination, and biases discovered in the platforms of big technology companies are commonplace today, and big tech's reluctance to disclose how they operate counteracts ideals of transparency, openness, and accountability. This book is for computer science students and researchers who want to study big tech's corporate surveillance from an experimental, empirical, or quantitative point of view and thereby contribute to holding big tech accountable. As a comprehensive technical resource, it guides readers through the corporate surveillance landscape and describes in detail how corporate surveillance works, how it can be studied experimentally, and what existing studies have found. It provides a thorough foundation in the necessary research methods and tools, and introduces the current research landscape along with a wide range of open issues and challenges. The book also explains how to consider ethical issues and how to turn research results into real-world change.
Conference Paper
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Modern smartphone platforms implement permission-based models to protect access to sensitive data and system resources. However, apps can circumvent the permission model and gain access to protected data without user consent by using both covert and side channels. Side channels present in the implementation of the permission system allow apps to access protected data and system resources without permission ; whereas covert channels enable communication between two colluding apps so that one app can share its permission-protected data with another app lacking those permissions. Both pose threats to user privacy. In this work, we make use of our infrastructure that runs hundreds of thousands of apps in an instrumented environment. This testing environment includes mechanisms to monitor apps' runtime behaviour and network traffic. We look for evidence of side and covert channels being used in practice by searching for sensitive data being sent over the network for which the sending app did not have permissions to access it. We then reverse engineer the apps and third-party libraries responsible for this behaviour to determine how the unauthorized access occurred. We also use software fingerprinting methods to measure the static prevalence of the technique that we discover among other apps in our corpus. Using this testing environment and method, we uncovered a number of side and covert channels in active use by hundreds of popular apps and third-party SDKs to obtain unauthorized access to both unique identifiers as well as geolocation data. We have responsibly disclosed our findings to Google and have received a bug bounty for our work.
Conference Paper
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We show that the MEMS gyroscopes found on modern smartphones are sufficiently sensitive to measure acoustic signals in the vicinity of the phone. The resulting signals contain only very low-frequency information (<200Hz). Nevertheless, we show, using signal processing and machine learning, that this information is sufficient to identify speaker information and even parse speech. Since iOS and Android require no special permissions to access the gyro, our results show that apps and active web content that cannot access the microphone can nevertheless eavesdrop on speech in the vicinity of the phone.
Conference Paper
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China is one of the largest Android markets in the world. As Chinese users cannot access Google Play to buy and install Android apps, a number of independent app stores have emerged and compete in the Chinese app market. Some of the Chinese app stores are pre-installed vendor-specific app markets (e.g., Huawei, Xiaomi and OPPO), whereas others are maintained by large tech companies (e.g., Baidu, Qihoo 360 and Tencent). The nature of these app stores and the content available through them vary greatly, including their trustworthiness and security guarantees. As of today, the research community has not studied the Chinese Android ecosystem in depth. To fill this gap, we present the first large-scale comparative study that covers more than 6 million Android apps downloaded from 16 Chinese app markets and Google Play. We focus our study on catalog similarity across app stores, their features, publishing dynamics, and the prevalence of various forms of misbehavior (including the presence of fake, cloned and malicious apps). Our findings also suggest heterogeneous developer behavior across app stores, in terms of code maintenance, use of third-party services, and so forth. Overall, Chinese app markets perform substantially worse when taking active measures to protect mobile users and legit developers from deceptive and abusive actors, showing a significantly higher prevalence of malware, fake, and cloned apps than Google Play.
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Information associated with regulatory compliance is often siloed as legal documentation that is not suitable for querying or reuse. Utilising open standards and technologies to represent and query this information can facilitate interoperability between stakeholders and assist in the task of maintaining as well as demonstrating compliance. In this paper, we show how semantic web technologies can assist in representation and querying of compliance information related to the General Data Protection Regulation (GDPR), an European law governing the use of consent and personal data. We focus on the subset of obligations related to the use of consent and personal data, and represent the associated metadata using the previously published GDPRov ontology and GDPRtEXT resource. We present a proof-of-concept demonstration (available online) where information is queried to automatically populate the GDPR-readiness checklist published by Ireland’s Data Protection Commissioner.
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The high-fidelity sensors and ubiquitous internet connectivity offered by mobile devices have facilitated an explosion in mobile apps that rely on multimedia features. However, these sensors can also be used in ways that may violate user’s expectations and personal privacy. For example, apps have been caught taking pictures without the user’s knowledge and passively listened for inaudible, ultrasonic audio beacons. The developers of mobile device operating systems recognize that sensor data is sensitive, but unfortunately existing permission models only mitigate some of the privacy concerns surrounding multimedia data. In this work, we present the first large-scale empirical study of media permissions and leaks from Android apps, covering 17,260 apps from Google Play, AppChina,, and Anzhi. We study the behavior of these apps using a combination of static and dynamic analysis techniques. Our study reveals several alarming privacy risks in the Android app ecosystem, including apps that over-provision their media permissions and apps that share image and video data with other parties in unexpected ways, without user knowledge or consent. We also identify a previously unreported privacy risk that arises from third-party libraries that record and upload screenshots and videos of the screen without informing the user and without requiring any permissions.
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We present a scalable dynamic analysis framework that allows for the automatic evaluation of the privacy behaviors of Android apps. We use our system to analyze mobile apps’ compliance with the Children’s Online Privacy Protection Act (COPPA), one of the few stringent privacy laws in the U.S. Based on our automated analysis of 5,855 of the most popular free children’s apps, we found that a majority are potentially in violation of COPPA, mainly due to their use of thirdparty SDKs. While many of these SDKs offer configuration options to respect COPPA by disabling tracking and behavioral advertising, our data suggest that a majority of apps either do not make use of these options or incorrectly propagate them across mediation SDKs. Worse, we observed that 19% of children’s apps collect identifiers or other personally identifiable information (PII) via SDKs whose terms of service outright prohibit their use in child-directed apps. Finally, we show that efforts by Google to limit tracking through the use of a resettable advertising ID have had little success: of the 3,454 apps that share the resettable ID with advertisers, 66% transmit other, non-resettable, persistent identifiers as well, negating any intended privacy-preserving properties of the advertising ID.
Conference Paper
The mobile app market is evolving at a very fast pace. In order to stay in the market and fulfill user's growing demands, developers have to continuously update their apps either to fix issues or to add new features. Users and market managers may have a hard time understanding what really changed in a new release though, and therefore may not make an informative guess of whether updating the app is recommendable, or whether it may pose new security and privacy threats for the user. We propose a ready-to-use framework to analyze the evolution of Android apps. Our framework extracts and visualizes various information ---such as how an app uses sensitive data, which third-party libraries it relies on, which URLs it connects to, etc.--- and combines it to create a comprehensive report on how the app evolved. Besides, we present the results of an empirical study on 235 applications with at least 50 releases using our framework. Our analysis reveals that Android apps tend to have more leaks of sensitive data over time, and that the majority of API calls relative to dangerous permissions are added to the code in releases posterior to the one where the corresponding permission was requested.