An Ontology for Enforcing Security and Privacy Policies on Mobile
Devices
Brian Krupp, Nigamanth Sridhar and Wenbing Zhao
Electrical and Computer Engineering Department, Cleveland State University,
2121 Euclid Ave, Cleveland, OH 44115, U.S.A.
Keywords:
Mobile Devices, Security and Privacy, Ontologies.
Abstract:
Mobile devices have experienced explosive growth and rapid adoption. These devices have also become troves
of security and privacy data of the consumers that utilize them. What makes mobile devices unique from
traditional computing platforms is the additional sensing components they contain and their ease of access
which allow consumers to make these devices a part of their lives. Additionally these devices are fragmented
in operating systems, sensing capabilities, and device manufacturers. In this paper we define an ontology that
can be utilized as a foundation for enforcing security and privacy policies across all mobile devices, and use
the ontology to define policies and to model knowledge elements for mobile devices. We also identify areas
where the policies can be applied, including whether to enforce policies on the device or in the cloud.
1 INTRODUCTION
Mobile devices continue to grow in prominence in
utilization across both consumers and businesses.
For vast number of consumers, mobile devices have
become their primary communication and enter-
tainment devices in their day to day lives. For
example Foursquare claims that there have been
over ve billion check-ins using their application
alone (Foursquare, 2014). Likewise, Twitter claims
an average of 5,700 tweets per second (Twitter, 2014).
Mobile devices are equipped with a variety of sen-
sors that may log the activities and locations of their
users. Hence, such data can be highly personal and
sensitive and must be protected by proper security and
privacy policies and mechanisms. The task of pro-
tecting such data is made even more challenging due
to the need of synchronization of personal data across
all devices using cloud based services such as iCloud,
OneDrive, Google Drive, etc.
The need for developing and enforcing security
and privacy policies on mobile devices can be demon-
strated by recent privacy leaks and security exploita-
tions. For example the popular social networking
application Facebook was found to leak a user’s
phone number without a user logging into the appli-
cation (Symantec, 2014). Another example regarding
the popular mobile game Angry Birds not only col-
lects user’s privacy data such as their location but this
data has also been targeted by agencies such as the
NSA (Ball, 2014) to profile users.
In this paper we build on previous research and
analysis of the most widely used mobile operating
systems to propose an ontology that can be used
across all mobile devices to enforce security and pri-
vacy policies for consumers. We define how to model
the vast amount of knowledge that can be gained from
mobile devices both static and inferred from observed
activity on the device. We then recognize how this
ontology can be utilized and implemented both within
and outside the device.
The rest of this paper is organized as follows. In
Section 2, we discuss the current state of the art in the
field of defining security and privacy policies for mo-
bile devices. In Section 3, we provide an overview of
our ontology, and describe it in detail in Sections 4, 5,
and 6. We then describe how the ontology could be
utilized and enforce user defined security and privacy
policies in Section 7. We conclude with a summary
of our contributions and some pointers to future work
in Section 8.
2 RELATED WORK
There has been various work in constructing ontolo-
gies around security and attack behavior with some
of these targeted towards mobile. However related
288
Krupp B., Sridhar N. and Zhao W..
An Ontology for Enforcing Security and Privacy Policies on Mobile Devices.
DOI: 10.5220/0005081502880295
In Proceedings of the International Conference on Knowledge Engineering and Ontology Development (KEOD-2014), pages 288-295
ISBN: 978-989-758-049-9
Copyright
c
2014 SCITEPRESS (Science and Technology Publications, Lda.)
work that focused on mobile has been limited. Beji
et al. proposed a security ontology towards mobile
devices and took a more traditional security approach
by defining semantics around common security ele-
ments including Asset, Vulnerability, Threat, etc (Beji
and El Kadhi, 2009b). They emphasize that mobile
devices lack standards in this area and the ontology
they proposed took a general approach without a fo-
cus on specific use cases. They then further extended
their ontology to take a knowledge based approach
(Beji and El Kadhi, 2009a). In both proposals the on-
tology was formally defined in Web Ontology Lan-
guage (OWL). Tsoumas et al. took a similar ap-
proach in focusing on security and proposed an on-
tology with a focus more on the security management
aspect (Tsoumas and Gritzalis, 2006). Their frame-
work also has a knowledge focus which pulls data
from different sources.
Wang et al. proposed an ontology where again
the focus was on security but more with an empha-
sis on vulnerability management (Ju An WangGuo,
2010). They model similar attributes as in Beji et al
with a focus on Attack, Attack Mechanism, Attacker,
Product Vendor, etc. With a focus on being able to
manage vulnerabilities, their goal was to identify pat-
terns from external threats and vulnerabilities more
formally and precise (Ju An WangGuo, 2010).
Woo et al. also focused on modeling vulnerabil-
ities but took a different approach where they pro-
pose an ontology that models behaviors (Woo et al.,
2013). They recognize the difficulty to detect a secu-
rity threat with the different methods a system can be
attacked, so they take the approach of looking at be-
haviors that would identify an attack. Their ontology
models classes such as Actions, Behaviors, Abstract
Behaviors, etc.
Focusing on defining policies, Uszok et al. pro-
posed KAoS which is a system of policy and domain
services that allow a computer system to enforce hu-
man expressible policies (Uszok et al., 2003). Pana-
giotopoulos et al. proposed PROACT (Panagiotopou-
los et al., 2010), an ontology focusing on privacy in
smaller components such as wireless sensor compo-
nents.
On the contrary to the work described above, we
propose an ontology that can be used to enforce se-
curity and privacy policies with a focus on mobile de-
vices using a knowledge based approach. Also the uti-
lization of the ontology we propose does not require
modification to the operating system and focuses on
both privacy and security concerns.
3 ONTOLOGY OVERVIEW
We propose an ontology that consists of the following
three main categories: Policy, Activity, and Knowl-
edge. The relationship between Activity and different
type of Knowledge are illustrated in Figure 1.
In the Policy category of our ontology, we model
the policies that a user would define for their mo-
bile device, including ones described in more detail
in (Krupp et al., 2013; Krupp et al., 2014).
In the Activity category, we model real time ac-
tivity on the device that includes elements such as
location activity, network activity, and device state.
Activity is essentially a holding for any activity data
that can be transformed into knowledge. Over time if
it is not transformed into knowledge, the data is dis-
carded. An example of this transformation would be
gathering several locations from the device during the
day. If this data doesn’t contribute to new knowledge,
it will be discarded.
In the Knowledge category we model existing
knowledge and inferred knowledge as defined below:
Existing knowledge models personal data on the
mobile device including photos, calendar events,
location, etc. The need to model this knowledge is
so that the policies defined by the consumer have
a consistent model to utilize when the policies are
being enforced.
Inferred knowledge is what can be inferred from
existing knowledge. Since existing knowledge
can be built both from activity and directly from
the user’s personal data on the device, inferred
knowledge may contain previous activity data that
was transformed into knowledge. Knowledge that
can be inferred includes areas such as usage pat-
terns, network activity, and movement of the user.
The two primary drivers of including this in our
ontology is both performance and space. In per-
formance, since inferred knowledge summarizes
a collection of existing knowledge and activity el-
ements, this saves the enforcing application from
having to analyze all elements that made up the
inferred knowledge each time it needs to utilize it.
Also in regards to space, inferred knowledge al-
lows us to remove existing knowledge elements
that may no longer be needed as the inferred
knowledge from the elements that made it would
no longer be needed. This would be an obvious
constraint on an implementation utilizing this on-
tology if it held a historical record of all activ-
ity and existing knowledge without ever disposing
the data. This additional subcategory in our ontol-
ogy prevents the ontology from losing the value of
what that historical data provided.
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Enforcement Service
Activity
Existing Knowledge
Inferred Knowledge
Privacy Data
(Photos, Contacts, etc)
Current Activity
(Location, Network, etc)
Inferred Knowledge from
Existing Knowledge
Mobile Device
Knowledge Built from Activity
Figure 1: Ontology relationship depicting how data that is
modeled exists from one state to another.
For this ontology we identified the most com-
mon knowledge and activity elements across the most
widely used mobile operating systems. We recognize
that there will be outliers that are not covered in this
ontology based on OS, device, and sensor capability.
However our primary goal is to provide an ontology
that can cover a broad spectrum of devices as opposed
to one set or the union of all attributes of mobile de-
vices. All of the ontology objects discussed above
will be described in greater detail in the following sec-
tions. For this paper we define the properties that on-
tology elements contain, their relationships with other
objects in the ontology, and examples of application
of the ontology. The ontology we propose here has
been formally defined in OWL using Protege.
4 POLICY
The Policy portion of our ontology is mainly de-
rived from our previous research building a frame-
work for enhancing security and privacy on mobile
devices (Krupp et al., 2013; Krupp et al., 2014). The
policies defined in the ontology are divided into three
main categories: Security, Privacy, and General. An-
other type of policy defined in our ontology is the
ChainablePolicy which links both a security policy
and a privacy policy to provide more fine grained poli-
cies.
4.1 Security
Our main goal in this category is to define policies
around data transferred or persisted, authentication
data, and ensuring that they are handled using the
proper security mechanisms such as transport layer
security or encryption. The policies defined here are
targeted for non-jailbroken devices but can be used for
jailbroken devices as well. Within the security cat-
egory we define DataPersistence, NetworkSecurity,
and UserCredential security policies.
With DataPersistence our primary objective is to
ensure that any data persisted on the device would be
properly secured using the minimum encryption level.
Additionally with the availability of syncing data to
cloud based storage providers, we add the ability for
a user to specify policies on permitting data to be
synced. This policy could be used within corporate
networks to ensure that sensitive data is encrypted on
mobile devices in case they are lost or stolen, and that
sensible data would not leave the local network.
The NetworkSecurity policy specifies where data
can leave the device as opposed to DataPersistence
that defines how data can be persisted on the device.
This policy contains properties that define what do-
mains the application can communicate to and any
transport layer security requirements on the transmis-
sion of data. This can be be used to restrict any pri-
vacy leakage of an application that sends data on be-
half of the application to third party servers (for exam-
ple, for the purpose of tracking user online behaviors).
Additionally for any sensitive information that is in
transit, a user can ensure that they are using transport
layer security.
Lastly with the UserCredential policy our primary
objective is to ensure that specific policies around a
user credential being transmitted over a network could
contain its own restrictions. With this policy, we al-
low a global setting that would allow the credential to
be used by all applications.
4.2 Privacy
Both iOS and Android provide general privacy con-
trols that allow an application access to user’s pho-
tos, contacts, location, and other personal data. These
controls provide an all or nothing access to a specific
class of a user’s personal data. Currently consumers
cannot define finer granular controls such as allowing
an application to only gather location data in specific
locations, restricting the access to certain photos from
an application, or not allowing access to a subset of
contact records. With the Privacy category, we aim to
provide these controls.
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We define two general categories for privacy poli-
cies which include policies around data that sen-
sors create and policies around data that users create.
Within the policies that users can create, we define
policies for Calendar, Contacts, and Multimedia. For
all policies we define a generic policy that specifies
read/write permissions to these data stores. Addition-
ally Calendar and Multimedia contain temporal re-
strictions and Multimedia also allows for spatial re-
strictions. For example, a user could define that an
application does not have access to photos that were
taken during certain dates or at certain locations. Fur-
thermore, the ontology contains policies that specify
whether or not a particular attribute such as timestamp
or location of an entity can be accessed by an applica-
tion. This is important because even if a user does not
allow an application access to location data, an appli-
cation can easily determine a user’s location patterns
based on the location attribute of the photos taken by
the user. With the Contacts policy there are several at-
tributes such as name, address, phone number, email,
etc. that we define individual settings to protect ac-
cess to each of these elements.
For all of the policies defined, a user can selec-
tively specify which calendar events, photos and other
multimedia, and contacts that cannot be accessed by
the application. This allows a user to ensure that a
sensitive piece of privacy information is not accessi-
ble by a mobile application.
By sensor data, we refer to the data generated by
various sensors equipped in a mobile device. While
devices can contain accelerometer and gyroscopes,
we find that location data is most critical and define a
robust set of privacy settings around location. Besides
allowing general read access to the location, we define
properties to allow the user to specify spatial and tem-
poral constraints on gathering the location. Spatial
constraints include specified regions that the user does
not want their location to be gathered. This policy
could be used to disable location gathering in highly
sensitive locations that a user would not want to share.
This policy could also enable a user to block out loca-
tion gathering at certain times during the day to pre-
vent an application from predicting the pattern of the
user’s movements. Additionally we allow the user to
define whether or not regional monitoring (geofenc-
ing) is allowed and when gathering the location data
if it should be anonymized, generalized, or provided
as bogus data. Bogus data may be provided when the
application requires a location to be provided to func-
tion, regardless of what that location data is. Gener-
alized would give a consistent location within a given
region for applications that do not need the specific lo-
cation to provide their functionality such as a weather
Policy
PrivacySecurity
Sensor Data
User Data
Multimedia
Calendars
Contacts
Location
Data
Persistence
Network
General
Chainable
Uses
Figure 2: Ontology relationship depicting how policy is or-
ganized in the ontology.
forecasting application.
4.3 General
The General policy encompass temporal and spatial
policies that would be used as described in the above
policies.
4.4 Chainable
The primary goal of a Chainable policy is to allow
a user to attach several security policies to a privacy
policy. This allows a user to specify that an appli-
cation can gather its location but gathered location
data cannot be transmitted over a network. The policy
also enables a user to allow an application to access a
user’s photos but not write them to the local disk un-
encrypted. A Chainable policy gives the user a deeper
level of control of their privacy data by allowing se-
curity and privacy policies to be combined together,
as shown in Figure 2.
5 ACTIVITY
With the Activity category our objective is to model
common activity that can be gathered from how the
user interacts with the device, and to transform such
activity into knowledge. Activity usually represents
the current state of the device. We specify the follow-
ing Activity policies: Network Status, Location, Bat-
tery, Network, Motion, Multimedia, and Bluetooth.
Most of these policies are self explanatory in what ac-
tivity they model, however some need additional clar-
ification below.
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Facebook TwitterLinkedIn General
Friend
Post
Connection
Update
Follower
Tweet
Contact
Message
Figure 3: Shows generalization of integrated application
data.
The Multimedia Activity policy does not contain
the same attributes as Multimedia Knowledge. It is
more concerned with if microphone/camera is en-
abled, or is being recorded or replayed. Network Sta-
tus Activity models what kind of network connection
the user has, and if they are connected using WiFi to
what SSID the device is connected. With Motion Ac-
tivity, we specify how motion data can be used to help
in determining location and movement from one loca-
tion to another.
6 KNOWLEDGE
The Knowledge category encompass both Existing
Knowledge and Inferred Knowledge. Existing Knowl-
edge is gathered from personal data on the device as
well as activities on the device, such as current loca-
tion, network activities, etc. From Existing Knowl-
edge, we can infer deeper knowledge, which we refer
to as Inferred Knowledge, such as the pattern of the
user’s movement with respect to the time of day, or
the kind of personal data the user typically shares with
others. In the following sections we further describe
this part of the ontology.
6.1 Existing Knowledge
Existing Knowledge is used to model data that exists
on the device that is to be persisted as part of the en-
forcement of a user’s given policy. Existing Knowl-
edge can be further divided into Contacts, Creden-
tials, Multimedia, Integrated Applications, Events,
and Sensing.
Contacts, Multimedia, and Credentials model user
supplied data, and they would need to be updated as
needed from what the user has on the device so that
when enforcing a policy, there is a model represent-
ing this data. Integrated Applications is more con-
cerned about applications that have ties into the oper-
ating system such as Facebook, Twitter, and LinkedIn.
The data that these applications provide can be gen-
eralized into a model that includes contacts and mes-
sages as depicted in Figure 3.
Events include Calendars and Task Lists. Both are
very similar in nature but have subtle differences in
that certain elements such as a date and time one must
contain and the other can contain. For example, both
a calendar event and a task list contain a title and some
additional notes. A calendar always has a date and
time associated with it while a task list can contain
those attributes as well if there is a reminder event to
be fired. Additionally both a calendar and a task list
item can have a location associated with it where the
calendar event could be the location of the event and
a task list could be a reminder that is triggered when
the user enters a geofenced location.
6.2 Inferred Knowledge
Inferred Knowledge constitutes valuable elements for
the enforcement of security and privacy policies spec-
ified for a mobile device. Inferred Knowledge in-
cludes Spatial Data, General Usage, and Network Ac-
tivity. Spatial Data further consists of Location at
Time of Day and Movement from Location to Loca-
tion. The former is built from gathered location ac-
tivity over time and can be used to predict the user’s
location given the time of day. The latter is also built
from location data and time, and can be used to pre-
dict the user’s movement behavior. Both allow the ap-
plication that enforces the policy (referred to as ”en-
forcing application” in later text) to determine if an
application is tracking the user’s travel patterns, and
prevent such personal data to be leaked. Additionally
both allow the enforcing application to predict where
a user may be at a given time and ensure that location
data is not leaked if the enforcing application does
not have access to the user’s current location. This
prediction would become more accurate as more lo-
cation activity data is provided.
General Usage captures the knowledge regarding
usage patterns. Time of Day Device is Used Most and
Location Device is Used Most can be used to help
find opportune times and locations when a device is
less utilized to carry out more resource intensive op-
erations such as synchronizing data from the device to
an enforcement application. Additionally the enforce-
ment application would know from the most utilized
locations and date times when it would need to allo-
cate additional resources.
General Usage also includes Application Used at
Location, Application Utilized During Day and Time,
and Application Currently Utilized. Application Cur-
rently Utilized is determined from the network activ-
ity on the domain, attempted personal data access, and
data persistence. This allows the application enforc-
ing policies to understand what mobile application is
currently active and enforce the appropriate policies.
If the enforcing application is uncertain what mobile
application is currently being utilized, it can use in-
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Inferred KnowledgeActivity
Inferred Application Currently Utilized
Inferred
Application Used
at Time of Day
Inferred
Application Used
at Location
Network Activity Location Activity
Figure 4: Various inputs into building inferred knowledge
from activity and previous knowledge.
ferred knowledge from Application Used at Location,
Application Utilized During Day and Time, to deter-
mine what application is active. In each scenario,
if there is a high degree of confidence from the de-
termined application, the detected application would
feedback into the inferred knowledge around applica-
tion utilization (Figure 4).
Network Activity includes Domains Visited at Lo-
cation/Day and Time, High Network Activity on Net-
work Level, Privacy Data to Send, and Large Privacy
Data Upload. By inferring what domains are visited
at a location and time, we can determine what appli-
cation is currently being utilized on the mobile device
if we are unable to determine it from the current ac-
tivity on the device. With inferring the domains, we
can then determine what data may be sent and what
policies to enforce. By inferring how much network
activity is expected given a type of network connec-
tion (Wi-Fi, Cellular), the enforcing application can
detect abnormalities in the amount of data that is be-
ing transferred and designate appropriate resources to
enforce the user’s policies. For example if a user usu-
ally only uploads several or more photos on a Wi-Fi
connection, if we infer that there is a high level of
network activity on a cellular connection we can in-
vestigate further to determine if the abnormality is vi-
olating any security or privacy policies.
By inferring what personal data is sent for a given
application, the enforcing application can expect what
additional personal data may be sent when that appli-
cation is used. This again allows the enforcing ap-
plication to anticipate activities, which strengthens its
enforcement of the policy. Large Privacy Data Up-
load can be inferred by looking for personal data that
may have been sent out over a longer period of time
by an application as opposed to being sent all at once.
The inferred knowledge can be used to detect stealth
attacks that leak privacy data.
7 ONTOLOGY ENFORCEMENT
In the previous sections we defined the ontology that
can be used to define security and privacy policies
across mobile devices. In this section we address how
the ontology can be enforced and applied as well as
address some potential issues in the implementation
of ontology enforcement. In this section we outline
a service-based ontology enforcement approach. Un-
like previous research on detecting leakage of privacy
information from a device which has traditionally re-
quired modification to the operating system, we aim
to remove this requirement to lower the barrier of ef-
fective enforcement of security and privacy policies.
We believe that having the enforcement as a service
outside of the device allows the user’s mobile experi-
ence to remain unaffected by not requiring consump-
tion of the the device’s power as well as not produce
delays in the user experience by performing computa-
tionally expensive operations on the device to enforce
the user’s defined policies.
7.1 Ontology Enforcement Service
An example implementation of the ontology enforce-
ment service is illustrated in Figure 5. The service
consists of two main components. The first compo-
nent is a web based proxy service that is responsible
for maintaining the model of the ontology and enforc-
ing the defined policies by examining the intercepted
data originating from the mobile device. The second
component is a client application that is responsible
for reading system-level data from the device that the
proxy service would be interested in, and uploading
the data to the proxy service. We describe these two
components in more detail in the following sections.
Note that all networked communications from the ap-
plications in the device are routed to the proxy ser-
vice.
7.1.1 Client Application
The client application on the device is responsible for
ensuring that system-level data on the device is syn-
chronized with the web based proxy device, as shown
in Figure 5. For example, when a user adds new pho-
tos on the device, the client application needs to en-
sure that this photo exists on the service as quickly as
possible. The client application is also responsible for
occasionally polling activity data on the device and
sending it to the service. The activity data is needed
for both knowledge building and for the proxy ser-
vice to enforce spatial and temporal parameters in the
predefined policy. Furthermore, the client application
is responsible for authenticating the remote service to
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Proxy Service
Ontology
Mobile Device
Proxy Service Client
Knowledge
Data
Activity
Data
Mobile Device
Applications
Networking
Proxy
Service
Data Collection Service
Data Data
Data
Network Requests
Internet
Request
Validation
Network Request
to Proxy
Populate and Transform
Validated Request
Validate
Validation
Result
Detected
Request
from Proxy
Service Client
Figure 5: Example implementation for enforcing ontology.
ensure that data is not leaked to a service imposing
as the dedicated service that the client is utilizing. It
is inevitable that there will be a large amount of data
synced to the proxy service from the client to facilitate
the modeling of a large amount of existing knowledge
in the ontology.
7.1.2 Proxy Service
For regular consumers, the proxy service can be of-
fered as a public cloud service. However for orga-
nizations that provision mobile devices to their em-
ployees, it may be more attractive to deploy the proxy
service on-premise where global policies could be de-
fined and the service could be easily scaled.
In either deployment scenario, the proxy service
is responsible for examining networked traffic from
the mobile device and determining if the data that it is
receiving are requests from the client application on
the device or requests from other applications on the
device, as illustrated in Figure 5. When updates are
received from the client application, they are passed
on to a data collection service that is responsible for
building the ontology model based on attributes of the
data that it receives. The data collection service may
need additional data from the client to make a deci-
sion, in which case, such information is indicated in
the response to the client.
If the request is detected to be from any other ap-
plication, the proxy service delegates the validation
of the request to a request validation service. The re-
quest validation service examines the data in the re-
quest and it determines if there are any corresponding
knowledge entities in the request using the ontology.
Based on this determination, it then locates the poli-
cies that correspond to the identified knowledge en-
tities in the request and validates whether or not the
request should be made. This is by far the most criti-
cal piece in the service and where our future research
will target as this service needs to be as efficient as
possible to ensure that the user does not experience a
noticeable delay.
7.2 Discussion
As we recognized earlier, the request validation com-
ponent of the proxy service may be computationally
expensive. This will be the primary focus of our fu-
ture research so that we can minimize the computa-
tion needed to allow for timely enforcement without
a perceived user delay. We also recognize that the
policies we defined in this ontology may not be ideal
for all consumers to create and manage. However,
consumers seeking the additional fine grained control
can add this service and build these policies. Alter-
natively, organizations that would have the proxy ser-
vice on premise could define and manage these poli-
cies for use across their entire workforce. In any sce-
nario, we recognize that managing the policies needs
to be as simple and intuitive as possible for user adop-
tion.
The client application must keep the proxy service
in sync with data that exist on the device. This re-
quirement raises two primary concerns. One concern
is that there must be a level of trust to the proxy ser-
vice as it would hold a large amount of data from the
device. This may seem impractical. However, cloud
based services that synchronize data across devices
are pervasive today, such as OneDrive, iCloud, Drop-
Box, and Google Drive. This does stress the impor-
tance of establishing trust with the proxy service and
performing authentication both on the client and the
service. The other concern is the ability to synchro-
nize the data efficiently and at periods that do not af-
fect the user experience. We envision that the client
application would have to be ”smart” and take advan-
tage of opportunistic periods where utilization is per-
ceived low and the device is either plugged in or has
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sufficient battery power. The most opportune periods
for performing sync can also be determined from the
inferred knowledge that is built on previous usage ac-
tivity.
Within the proxy service there would also need to
exist a background service that performs necessary
transformations of activity data into existing knowl-
edge and existing knowledge into inferred knowledge.
Similar to the client application, this transformation
service should take advantage of periods that are de-
termined to be most opportunistic to perform these
transformations so that the user’s experience is un-
affected. From these transformations, validation of
intercepted network requests should become more ef-
ficient during more active periods.
8 CONCLUSION AND FUTURE
WORK
In this paper we proposed an ontology by examining
previous research and analyzing the most widely used
mobile operating systems for common attributes. The
ontology we propose can be used to enforce secu-
rity and privacy policies on a mobile device using a
knowledge based approach. The key difference in the
ontology we propose here compared with previous re-
search is that it focuses on enforcement of policies as
opposed to detection of security vulnerabilities as was
the primary focus of the related work we identified.
Additionally the ontology we propose along with how
it can be enforced does not require modification to the
operating system. Our ontology also focuses on pri-
vacy concerns as well as security concerns.
For elements in the ontology such as activity and
existing knowledge, we recognize that personal data
and sensing components would have to be made avail-
able to the enforcing application. The determination
of possible policy violations can be performed locally
at the mobile device, or via a cloud service. We are
working on implementing such a cloud enforcement
service based on the proposed ontology. We take this
approach because it may consume less resources for
policy enforcement compared with the local enforce-
ment approach. Furthermore, with most modern mo-
bile operating systems implementing sandboxing con-
trols, any enforcing application that lived locally on
the mobile device would need to violate the sandbox-
ing mechanism to inspect data being leaked from the
device to enforce the user’s defined privacy and secu-
rity policies. Therefore, a cloud based enforcement
service is more feasible, which will be our focus in
future work.
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