=Paper=
{{Paper
|id=Vol-1316/paper1
|storemode=property
|title=Semantics for Privacy and Shared Context
|pdfUrl=https://ceur-ws.org/Vol-1316/privon2014_paper1.pdf
|volume=Vol-1316
|dblpUrl=https://dblp.org/rec/conf/semweb/YusPDFJM14
}}
==Semantics for Privacy and Shared Context
==
https://ceur-ws.org/Vol-1316/privon2014_paper1.pdf
Semantics for Privacy and Shared Context
Roberto Yus1 , Primal Pappachan2 , Prajit Kumar Das2 ,
Tim Finin2 , Anupam Joshi2 , and Eduardo Mena1
1
University of Zaragoza, Zaragoza, Spain
{ryus,emena}@unizar.es,
2
University of Maryland, Baltimore County, Baltimore, USA
{primal1,prajit1,finin,joshi}@umbc.edu
Abstract. Capturing, maintaining, and using context information helps
mobile applications provide better services and generates data useful in
specifying information sharing policies. Obtaining the full benefit of con-
text information requires a rich and expressive representation that is
grounded in shared semantic models. We summarize some of our past
work on representing and using context models and briefly describe
Triveni, a system for cross-device context discovery and enrichment.
Triveni represents context in RDF and OWL and reasons over context
models to infer additional information and detect and resolve ambiguities
and inconsistencies. A unique feature, its ability to create and manage
“contextual groups” of users in an environment, enables their members
to share context information using wireless ad-hoc networks. Thus, it
enriches the information about a user’s context by creating mobile ad
hoc knowledge networks.
Keywords: Context, Semantic Web, ontology, OWL, reasoning
1 Introduction
A recent study by Cisco [5] predicts that the number of mobile devices in the
world will exceed the population by 2014 and that 54% of them will be smart
devices by 2018. In addition to smartphones, other mobile devices like tablets or
wearable computing devices (e.g., smart watches and glasses) are also becoming
ubiquitous.
These smart devices and their applications store and have access to a great
deal of personal information about their users. Since communication is a primary
use, they know their users’ contacts, phone activity, email correspondents, and
social media connections. They have a rich array of sensors that track location
and activity and installed software applications can access the device’s audio
and video streams. Applications may also be able to extract information from
stored content that includes users’ calendars, email, and social media streams.
This data can be integrated, combined with background knowledge, and rea-
soned over to produce even more information. Capturing, maintaining, and using
such information can help mobile applications provide better services by being
2 R. Yus et al.
aware of their user’s context. However, much of the information is personal and
sensitive and must be protected from undesired use or disclosure. As a result,
there are two related problems inherent in the ecosystem of smart devices, sen-
sors, applications, and users.
The first problem involves protecting the privacy and security of personal
data. For example, Android, the most popular mobile operating system as of
2014, follows a single “take-it-or-leave-it” install-time permission acquisition
model for handling user data security. This approach assumes that users are
able to understand the various permissions requested by application and differ-
entiate between what is acceptable and what is not. Given the volatile nature of
personal data, mechanisms which allow flexible management are important to
protect privacy and security of the users. To address this, we have been exploring
the use of policy-based, context-dependent privacy and security frameworks that
manage personal data and access to it. Our frameworks use semantic technolo-
gies including Semantic Web representation languages (OWL and RDF) and
tools for reasoning with and enforcing context-dependent information sharing
policies [4, 8, 12, 13].
The use of ontologies for defining contextual concepts such as activity and
location enables us to apply our policies over generalized or specific instances of
these concepts. It also allows us to define more fine-grained policies for control-
ling various aspects of information sharing such as what information is shared,
with whom it is shared, and in which context. For example, enabling a user to
define a policy rule stating that social network apps cannot access a recording
device whenever she is in a research meeting with her colleagues. Additionally,
in situations where information sharing cannot be stopped, it can also support
obfuscation mechanisms, for example showing your location with a 100 kilometer
accuracy when you are not at work or are outside normal working hours.
Since the framework requires a rich notion of context to enforce policies, the
second problem is how to obtain precise information about a user’s context. In
previous work [22, 23] we explored how to recognize and distinguish high-level
descriptions from low-level sensor data. For example, using supervised learning
techniques we can determine a user’s activity (e.g., attending class) and the role
she is playing in it (e.g., student). By accessing background knowledge available
from resources like DBpedia and Geonames we can map GPS locations to typed
places. For example, from coordinates (39.253798,-76.714354) we can learn that
we are in the ITE building which is part of UMBC which is an EducationalIn-
stitution and located in Maryland.
Such context information can help applications match user requirements to
available services. For example, by using the location of a user, applications like
Foursquare or Yelp can recommend restaurants. Activity tracking applications
like Endomondo allow users to track their daily activity and help them stay fit.
Systems like SHERLOCK [21], which is a distributed architecture for mobile
devices, provide users with information about potentially interesting services in
the vicinity taking the context of the user into account. Context awareness has
been useful in community health care scenarios as well [16].
Semantics for Privacy and Shared Context 3
Research in context-aware computing has predominantly focused on context
synthesizing [17] high-level context information from low-level information such
as sensor or user data [9, 14] (e.g., to infer that the user is in a meeting after
considering the data from sensors such as microphone, GPS, and the calendar
entry as busy). However, not all mobile devices have all the sensors nor does the
user provide their devices with all the data. The issue of data availability can be
overcome by sharing relevant context pieces among users from devices nearby,
which can help in creating a shared context model for these devices. For example,
my device may know I am located at UMBC but not know what activity I am
engaged in. Devices of nearby students may know and be willing to share the
fact that they believe they are attending a class with name Operating Systems
in room 231 of the ITE building. We can use this knowledge of commonality of
context across devices for building a richer shared, semantic context model.
The remainder of this paper is organized as follows. In Section 2, we intro-
duce Triveni, a system that allows collaborative information to be shared within
a group of devices, and present a motivating use case. In Section 3, we show the
high-level architecture of the Triveni system and the acquisition of context infor-
mation and its integration performed by the system. Finally, Section 4 surveys
related works and conclusions and future work are presented in Section 5.
2 Exploring Collaborative Context Discovery
Triveni3 is an experimental framework that allows a group of devices to discover
one another and share context information by combining existing techniques of
ad hoc network management, data access control, and semantic data manage-
ment. The potential is that all of the devices will benefit from a richer and more
accurate model of their context. Information privacy is protected by policies
running on each device that decide what information is made available to other
devices.
Triveni builds knowledge ad hoc networks [11] to enable mobile devices to
create “contextual groups” among them and exchange relevant knowledge in
a secure and private manner. Our Triveni prototype implements methods to
gather and integrate high-level context pieces from multiple devices to produce
an enriched context which is available for all participating devices. The use
of OWL ontologies and Description Logic reasoners lets it detect and resolve
conflicts and inconsistencies in the shared context, which occurs due to lack or
misinterpretation of low-level data and device failures.
2.1 Motivating Use Case
Jeff, Abed, and Annie are students at Greendale Community College (GCC) and
members of a study group that meets on Wednesdays in the study hall of the
3
Triveni Sangam is a confluence of three rivers and the point of confluence is said to
flush away all of one’s sins.
4 R. Yus et al.
library. On a given Wednesday, Pierce, who is not part of the study group, is
using his computer in the study hall.
There are five mobile devices on this particular Wednesday (see Figure 1):
Jeff’s tablet and smartphone, Abed’s laptop, Annie’s tablet, and Pierce’s lap-
top. Notice that these mobile devices are equipped with different number of sen-
sors (from smartphones that have compass, accelerometer, gyroscope, and GPS,
among others, to laptops that do not have any of these sensors). In addition,
the information that users provide can also be different and in varying detail.
In our specific scenario, Annie’s and Abed’s calendar entries give information
about meeting scheduled for that day (e.g., duration, topic, and participants)
and Jeff’s smartphone just used Foursquare to check in Study room F. With the
information available on their devices, traditional context generation systems
will create different high-level context information for each device (see Figure 1
where the current context of each user is shown in blue boxes).
Notice that some of the contexts obtained by the devices are wrong. For
example, Annie disabled the location gathering mechanism of her tablet, while
at home, to save battery and so, her device thinks that the location is still
“home”. Summarizing, the devices have some information about the context
but most of them are not as rich in detail as it would be desired.
Fig. 1. Motivating use case: users being part of a study group.
In this scenario, the best possible context for these mobile devices would the
following:
– For everyone in the study group (Jeff, Abed, and Annie) → “study group
about Spanish with a duration of one hour with three participants: Jeff, Abed,
and Annie”.
– For everyone in the library (Jeff, Abed, Annie, and Pierce) → “Study room
F inside Greendale Community College at 25◦ C and with the lights on”.
– For every device of Jeff (Jeff’s tablet and smartphone) → “heart rate 70bpm”.
Semantics for Privacy and Shared Context 5
However, not every mobile device has access to the information needed to
compute the fully enriched context. Nevertheless, the collaboration among them
can be used to address this problem.
3 Architecture of the System
Triveni’s primary goal is to enrich the information about a user’s context, ob-
tained by context synthesizers, by leveraging the context of other users nearby4 .
This way, applications are able to make use of the enriched context provided
by the system. Triveni has a decentralized architecture where mobile devices
communicate among themselves using wireless ad hoc networks and exchange
their context (see Figure 2 for the high-level architecture of each Triveni node).
Therefore, Triveni:
1. Obtains the context information (see Section 3.1) from: 1) the available Con-
text Synthesizer(s) (Context Manager module); and 2) devices discovered in
the vicinity (Communication module).
2. Reconciles the context information collected (see Section 3.2) to generate
the shared context models (Integration module) verifying the information
integrated to resolve inconsistencies (Inconsistency Resolving module).
Triveni uses Semantic Web technologies (specifically OWL ontologies and
semantic DL reasoners) for context modeling. This allows Triveni to detect in-
consistent information by using the reasoner and also to infer additional infor-
mation which has not been explicitly stated. Another benefit of using ontologies
to model context is that it would be possible to reconcile different context model-
s/definitions by using ontology alignment techniques [18]. Ontologies have been
widely used before to define and extract context [3]. However, for the sake of
simplicity, we consider that mobile devices in our system use a common ontol-
ogy for context definition (see Figure 3 for an excerpt of the ontology defined
for our use case). We advocate using a local reasoner, a program that infers
logical consequences from a set of asserted facts or axioms [7], on each device.
The use of semantic reasoners (and Semantic Web technologies in general) on
mobile devices has been studied in [20] and our results show that today’s mobile
devices can handle semantic reasoning.
3.1 Context Acquisition
To acquire information about the context of a user, Triveni modules running on
each device first obtain the high-level context of the user using context synthe-
sizers, specifically those modeling the context using ontologies [3]. Such context
synthesizers can have varying degrees of certainty with regards to the accuracy
of the high-level context based on factors such as: 1) the sensors used in ob-
taining the data; 2) liveness inferred from sensor update frequency; and 3) the
4
We refer to users and their mobile devices interchangeably along the paper.
6 R. Yus et al.
Fig. 2. High-level architecture of a node using the Triveni framework.
accuracy versus power trade off considerations. Triveni uses context synthesizers
which can provide a measure of confidence for RDF relations, which gives the
probability that the high-level context fact is true [2, 17].
Triveni leverages the context information provided by nearby devices to en-
rich the context model of the user. For this task, the system creates ad hoc
wireless networks to communicate with other devices and to exchange context
information. The system considers short to mid range communications to dis-
cover users nearby (e.g., the same room) because, in general, nearby users share
the same location and activity. While same activity could be performed by more
users located outside this range, this is out of the scope of this paper.
When connecting with other devices through wireless networks, Triveni must
ensure that no eavesdroppers take part in the communication. We use Diffie-
Hellman key exchange for sharing the secret key which can be further used in
encrypting information shared between Triveni devices. This shared key can be
used in symmetric encryption techniques such as Advanced Encryption Standard
or Blowfish. Thus we utilize a decentralized key agreement protocol similar to [1].
Triveni then creates contextual groups among the devices connected enabling
Semantics for Privacy and Shared Context 7
Fig. 3. An excerpt of the ontology used in our sample scenario.
devices to exchange contextual information that is interesting for them and thus
(1) limit the amount of data to share, and (2) allow them to share additional
details only with mobile devices of the group.
Once this network of mobile devices has been established the next step is to
exchange context information among the members. For this purpose, a mobile
device requests context information from other devices in the group. The device
which receives the request replies with the appropriate information taking into
account the user defined policies, based on various context options, to determine
what pieces of context are shareable, under what contextual situations, and with
whom. The idea of using context-aware policies for sharing of any private user
data was explored in our previous work [12].
3.2 Context Reconciliation
When mobile devices using different context providers and synthesizers exchange
context information, some of which is possibly imprecise, sometimes there can
be divergent information. For example, consider Annie’s tablet which receives
information about the location such as GCC and Study room F while her own
device thinks the location is Home (see Table 1). In this situation and for each
piece of context, the system has to determine from all the possible values which
ones are most likely.
8 R. Yus et al.
Identity Location Source Confidence kConfidencek
Annie Home Calendar 0.75 0.23
GPS and
Abed GCC 0.8 0.25
Geonames
Foursquare
Jeff Study room F 0.7 0.22
and GPS
Pierce GCC IP address 0.9 0.29
Table 1. Contextual information shared about location.
Triveni uses the semantic reasoner and ontology to deduce if a given fact
is supporting a different one. For example, Jeff’s smartphone states that its
location is Study room F and so, Jeff’s device is implicitly supporting that its
location is GCC. Therefore, in the example of Table 1 three devices support
that the location is GCC (Pierce’s, Abed’s, and Jeff’s). The same situation can
arise with activities, both Abed’s and Annie’s devices share that the activity
performed is a tvn:Study Group and so, they support the tvn:Meeting activity
shared by Jeff’s device.
For each different context piece, cpx , (e.g., cploc for location) we have to
compute a global confidence on each of the different facts shared, fi , (e.g., GCC,
Home, and Study room F) taking into account that some of them can be sup-
ported by more than one device (e.g., GCC is supported by Pierce, Abed, and
Jeff as mentioned before). Then, let T be the set of normalized confidence values
related to a piece of context cpi , and let S be the set of normalized confidence val-
ues that support a context value fi (e.g., location facts from Abed’s and Pierce’s
devices support location as GCC ). We sum up the values in S and normalize it
over T , to compute the global confidence gci , as follows:
P
nci
gci = P i ∀ci ∈ S, ncj ∈ T (1)
j nc j
After the context integration process, the system will finally obtain a list
of candidate context pieces with their computed confidence, GC(cpx ). In our
previous example, the final possible locations computed for the users along
with their confidences are: GC(cploc )={Home(0.23), GCC(0.77), Study room
F(0.22)}. Notice, that there is an inconsistency in this shared primary context
information as there are two conflicting locations present, namely Study Room
F/GCC and Home.
To detect semantic inconsistencies, constraints should be modeled in the on-
tology. For example, in the context ontology that we defined for our use case
(see Figure 3) we stated that a user can only have one location (by defining the
tvn:hasLocation property as functional), and that the activity classs tvn:Standing
and tvn:Running are disjoint. Triveni uses the context facts along with their con-
fidence values for inconsistency detection and resolution. For each piece of con-
text, cpx , and the list of possible values, GC(cpx ), the system reorders GC(cpx )
according to the confidence computed for each element in descending order.
Semantics for Privacy and Shared Context 9
The list of possible locations in our example will be reordered to GC(cploc ) =
{GCC(0.77), Home(0.23), Study room F(0.22)}. Then, for each element of GC(cpx ),
it creates an axiom and materialize it in the local ontology and use the reasoner
to check whether the ontology is still consistent. In the case of the reasoner
inferring that the ontology is inconsistent, Triveni removes the last axiom mate-
rialized because its confidence will be lower than previous one(s).
In some scenarios it is possible that only a few devices share interesting and
precise information and so the confidence computed for them will be low (e.g.,
in our previous example only Jeff shares that the location is Study room F and
then the confidence computed is the lowest). However, it is also possible that
this low confidence is caused by wrong information being shared. A variety of
approaches could be followed to tackle this problem, from conservative solutions
(only use the context with the highest confidence) to optimistic approaches (use
all context pieces that are not inconsistent). Our system uses a semi-optimistic
approach: use all context pieces that are not inconsistent and whose confidence
is greater than a threshold value.
4 Related Work and Discussion
Context-aware computing is a very active field. A survey of the literature [2]
shows that context extraction and user context generation from mobile sensors
or other sources, has been well studied. The techniques proposed can be broadly
classified into two categories: the first relies on machine learning models to learn
about features from sensor data to predict the user context [14]; the second
focuses on defining context using ontologies and rules and uses a reasoner to
infer associations between sensor data and user context [3].
In our work we avoid low-level context extraction and focus on using peer-
to-peer (P2P) networks of devices to share high level context information and in
a context enrichment process. Wibisono et al. [19] also leverages P2P networks
of devices in context-awareness computing. However, in their approach, devices
in a specific location (e.g., a room) are used to detect the “situation” there (the
situation concept they use is similar to the activity concept used in this paper).
They integrate low-level sensor information and use machine learning techniques
to reason the most probable situation from the previously defined list of situa-
tions for the room. In our approach, we consider high-level context information
shared by the devices and base our integration on semantic techniques (ontolo-
gies and a semantic reasoner). In addition, we do not start with a set of possible
situations for a location. Finally, the contextual groups enable us to limit the
information that the devices in a P2P network share.
The idea of using user groups to share information, like in a meeting, dis-
cussion or party was explored in [10]. They used activity history and contact
information to suggest relevant groups. In this paper we have explained the idea
of a data-driven need-based contextual group formation. In another work done by
Lane et al. [15] crowd-sourcing to correct context classification errors and label
sensor-data was exploited. The idea of Community Similarity Networks (CSN)
10 R. Yus et al.
proposed in [15] is however, significantly different from contextual groups defined
in this paper. While CSN uses similarity dimensions (based on lifestyle, physical
differences and sensor-data) to cluster users, contextual groups are based on the
primary pieces of context. Also CSN uses a centralized classification approach
to context recognition thereby creating a single point of failure, while contextual
groups are distributed in nature.
5 Conclusion and Future Work
A richer notion of context is required for providing relevant services to users and
protect their privacy at the same time. We described a system for cross-device,
semantic context management that enriches intra-device context. By gathering
context pieces collaboratively and reasoning over them to discover and resolve
inconsistencies, the system is resilient in the face of missing or erroneous sources
of information.
The resulting shared context models have the potential to be more complete
and accurate than any of the participating intra-device models. Using the Triveni
system, mobile devices perform two key activities: 1) performing cross-device
context discovery and integration to create a richer context model using the
shared information, and 2) using secure communication and semantic policies to
facilitate the exchange of context information within contextual groups.
As future work we plan to evaluate the effectiveness of our system through the
development of a prototype. Also, we plan to incorporate transition of context
in the groups as well as the collaboration among different groups for further
enrichment. Finally, we plan to take into account the trade off between energy
consumption and creation and maintenance of contextual groups [6].
Acknowledgments. This research work was supported by the CICYT projects
TIN2010-21387-C02-02, TIN-2013-46238-C4-4-R and DGA-FSE and NSF grants
0910838 and 1228198.
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