Service Sharing with Trust in Pervasive
Environment: Now it’s Time to Break the Jinx
Sheikh Iqbal Ahamed
Munirul M. Haque
Nilothpal Talukder
Department of Math., Stat., and
Computer Science
Marquette University,
Milwaukee, Wisconsin, USA
Department of Computer
Science
Purdue University, West
Lafayette, Indiana, USA
Department of Math., Stat., and
Computer Science
Marquette University,
Milwaukee, Wisconsin, USA
iq@mscs.mu.edu
mhaque@cs.purdue.edu
ntalukde@mscs.mu.edu
ABSTRACT
In such as a highly dynamic and open environment, it has
become a challenge to deploy multiple context-sensitive
services due to the unwillingness of service provider to share
resources. This apprehension to share resources stems mainly
from a lack of trust. Sometimes infrastructure plays pivotal role
to solve this issue with dynamic access control to replace
traditional static policies. But when it comes to effective
resource sharing in an infrastructure-less environment we face
problems such as poor storage and computational capability. In
this paper, we have developed a lightweight and distributed
trust model based on recommendation, which will guarantee
that service providers can securely share an unlimited number
of resources, limited only by their hardware and bandwidth
limitations. The multi-hop recommendation protocol
incorporates a flexible behavioral model to handle interactions
during service sharing and usage. This protocol will also assess
risk using recommendations from context-sensitive services, in
the trust framework, to help ensure smooth access to resources
and services.
Keywords
Access Control, Trust Model, Malicious recommendation,
student’s t-distribution.
1. INTRODUCTION
The open and dynamic nature of the pervasive computing
environment allows entities to join and leave frequently. This
causes a large number of autonomous entities to interact in an
ad-hoc manner, raising concerns as to the trustworthiness of the
service providers. Thus service providers are not willing to
share their resources to the anonymous nodes for fear of a
potential security violation. To handle the issue of anonymous
nodes, a trust management system must grant user access to
resources and information based on trustworthiness rather than
the conventional technique map access rights [1] authorization.
However, in an environment lacking infrastructure, as such an
ad-hoc network, it is a challenge to deploy a dynamic access
control due to the lack of memory and computational power of
the small handheld devices that make up the network. To meet
the security goals in a pervasive resource sharing environment,
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the model has to be able to deal with devices and environment
of unknown origin and also has to be adaptive to the dynamics
of mobile and socially motivated computing [1]. For this
reason, commonly used mechanisms such as role-based access
control and public key infrastructures have trouble adapting to
these requirements, because they require a predetermined list of
entities to properly function. Society, for example, uses trust
and reputation to empower a person with pre-established rights.
This trust is a dynamic parameter which evolves over time. Our
goal is to create a computational model to emulate the complex
social mechanism of trust.
Several researchers have focused on develop dynamic access
control policies, and more specifically on event and location
context. Researchers in MIT’s CSAIL lab took a semantic
approach in developing a model for context-aware dynamic
access control framework [2]. The efforts in [3, 4] mostly
emphasized the event and location contexts. The model in [5]
uses central authority, which is a system where reliability and
accountability are most important, such as healthcare
applications.
Much research has also been done on trust models in a pervasive
computing environment. Several well known trust models
depicted in [7, 9, 15, 16, 17] do not adapt well to the pervasive
computing environment due to problems including complex
computation model (using different cryptographic and secret key
sharing techniques) such as [9] (threshold cryptography), [15, 16,
17] (certificate, key binding, and trust relationship). These models
utilize protocols to heavy in weight for pervasive devices with
limited storage and energy. Due to the presence of fixed
infrastructure or central server trust models [15] (a fixed device
has to act as security agent in each domain) and [7] (requirement
of central key server) are inappropriate for the pervasive
environment. A probabilistic approach has been adopted in the
Beta Reputation System [34], B-trust [23], and Learning Model
[21]. Along with these models, a large number of recent trust
models fall into the distributed and dynamic category [25-33].
However, none have explored a protocol to handle malicious
recommendations with the exception of the learning model [21].
However, their approach, does not change the overall
recommendation value.
Our approach addresses the issue of sharing resources in a
pervasive environment by incorporating dynamic access control
with a trust framework. We focus on developing a
recommendation based trust model that will allow the dynamic
property of trust to go beyond the traditional methodologies of
static role based access control. Our model is also capable of
handling malicious recommendations by incorporating a
statistical model to help make the access control more reliable and
flexible for an open environment. This model will work in an
open and dynamic environment where entities frequently join and
leave, don’t have any presets access values and don’t require any
central authority to issue a certificate.
Contributions of this paper: We have summarized the
contributions of the paper here:

The major contribution is the secured service sharing
solution for the open and dynamic pervasive
environment.

The solution is based on a Hop-based recommendation
trust protocol which provides support for the dynamic
evolution of trust through a behavioral model.

The trust based model also attempts to identify malicious
users and weeds out using trust measures.

We have also identified a suitable list of index for
measuring the users’ willingness to share services in an
open environment. The index is used to demonstrate user
experience in the evaluation section.
The outline of the paper is as follows: The overview of the trust
model has been presented in section 2. The summary of malicious
recommendation handling is presented in section 3. Related
works on trust are further discussed in section 4. Section 5
presents a user evaluation of our system prototype. Our future
plans to extend the model are mentioned in section 6.
2.
RESOURCE SHARING THROUGH
DYNAMIC ACCESS CONTROL WITH
TRUST FRAMEWORK
2.1 Why Trust
Framework
in
Access
Control
Consider a scenario in which node A wants to share or to get
access to node B’s resources. The first thing B will do is to reason
about the trustworthiness of A. B will accomplish this by
analyzing accumulated data from the previous interactions or
requesting some recommendations from his trusted parties in the
case that A has not had any interactions with B before. There may
also be a situation where there might not be enough information
to trust, then B has to make his decision based on other variables
[1].
Dynamic Access
control
Shared Services
Decision
Model
Trust Model
through a weighted average of direct trust and recommended
trust. A decision module is responsible for making a decision
based on the trust value and will directly contribute to the policy
of the access control.
Decision Model
Overall Trust
Direct Trust
Recommended Trust
Recommendation Protocol
Experience
Recommendation
Active
Passive
Discrete
Context
Fig. 2. Architecture of our Formalized Trust Framework
We consider the range of trust value as [0,1]. At first when a
node joins a network, it gets an initial trust value of 0.5. Since this
node does not have any prior interaction records or known
history, they can be neither trusted nor distrusted.
2.2.1 Direct Trust
Direct trust evolves from a node’s direct experience with other
nodes. As a node interacts with other nodes in the network, its
direct trust value for each of the other nodes changes based on the
satisfaction level of the interactions. It is the most reliable portion
of overall trust. This direct interaction in Figure 3 is shown by a
direct link between A and B in the topology of interaction
records.
2.2.2 Recommended Trust
Recommended trust is used in absence of a direct trust value and
is obtained when one node uses suggested trust values from the
nodes with which it has direct trust. In figure 3 A might want to
have a recommendation from B whether or not to serve C.
We devised a general equation for the calculation of
recommended trust. Let’s consider a node ωz requests a context ci
from ω1. If ω1 (Service Provider, or SP) does not have a direct
trust value for ωz (Service Requester, or SR), then it needs to
know the recommended trust value to make the context sharing
decision. Let us assume that there are n paths (p1, p2,
p3,……..,pi,…,pn) with a hop length greater than 1 from ω1 to ωz.
Context
Service
Delivery
Fig. 1. Integration of Trust Model into the Dynamic Access
Control Framework
Because B cannot also allow access to his resources for an
indefinite amount of time, his access policies will be dynamically
updated on the information based on trust over time. The service
delivery agent running on B will consult the access control to
decide on access. If trust values are satisfactory A is immediately
provided access. The interaction will also be used to modify the
existing trust status of A.
2.2 Trust Framework
First, we will highlight the points on formation of trust in out
model. We define the term “trust value” as the varying degree of
trustworthiness. Both types of trust values (direct and
recommended) have evolved from pre-evaluated contexts.
Each node will have its base level for trust with some other
nodes recalculated after each interaction. This base level will
directly contribute to the trust values. Overall trust is formed
T (1,  2)  T ( 2,  3)  ...  T (x, y )  T (y, z )
)

(  1) 
 (1 
) .................................(1)
10
Tpi  (
Where, 1,  2,...,x, y, z are the nodes on the path pi
from SP (ω1) to SR (ωz).
D(xT (ci)y),

T (x, y)  
where
D(xT (ci)y)  
xTy, otherwise

 = Hop distance between ω1 (SP) and ωz (SR)

=Distance based aging factor
The recommended trust value of ω1 on ωk is calculated as:
n
R( 1T (ci )z ) 
 Tpi
i 1
n
………………………(2)
The term (1 
(  1) 
) has been used as a weight factor to
10
satisfy the ‘distance based aging’ property. Justification of
can be used from Ahamed et al.’s approach [35].
D
F
C
E
H
L
B
M
A
K
J
N
P
G

I
O
10
Fig. 3. A topology of nodes with trust relationships
2.2.3 Active, Passive and Discrete Recommendation
Active recommendation is possible only from neighboring
nodes, for Passive recommendations the node might consider all
path that has hop length >=2. Again when a SP node can’t reach
any path to consider it for recommendation, it needs some way to
resolve the issue. That’s what we term discrete recommendation.
For the same context ci it considers recommendation from other
nodes that are same discreet graph relative to SR. In Figure 3, if A
needs a recommendation for N, the recommendation values for
the paths {M,N},{I,N}, and {P,N} are considered.
Here
equation
(1)
takes
the
following
Algorithm for HBRReq:
begin
for (all neighboring nodes of SP)
UNICAST(HBRRequest(Req_ID,SP_ID,
SR_ID,IH,IR,TS))
while(until response from all nodes or timeout )
begin
R= Receive(HBRRep(Req_ID,Rec_ID,RH,TR,TS));
Rec_Wo_Aging = (TR/(IH-RH)) ;
if (Rec_Wo_Aging >=0.5)
begin
Rec_W_Aging = Rec_Wo_Aging * (1  ((IH  RH )  1)  )
form
T (i, z )
Tp i  (
) 

 T (i, z )  [   1, Considerin g only 1 hop paths]
Since we are getting recommendations from nodes that are in no
way connected to SP, we used  =0.5 which is a relatively lower
weight factor. A later equation (2) is used to find the discrete
recommendation value.
2.3 Hop Based Recommendation Protocol
(HBRP)
We are using a hop based recommendation protocol to determine
trust values to consider a node eligible for access. Here we used
the following notations:
SP = Service Provider, SR = Service Requester, Req_ID =
Request ID, Rec_ID = Recommender ID, IR = Initial
Recommendation, TR = Total Recommendation, IH = Initial Hop
value, RH=Reduced Hop value, TS = Time stamp. This protocol
actually includes mechanisms for active and passive
recommendations. The hop field (IH) defines the maximum path
length which enables a node to avoid a long chain of
Table 1: Upper-lower limits for different confidence
values
Confidence
LCL
UCL
Standard Mean
Level (%)
Deviation
90
0.645381
0.886619
95
0.766
0.61715
0.91485 0.2082
99
0.55216
0.97984
recommendations. This value is reduced in each hop by 1 and the
path is ignored when the field becomes 0. The IR field contains
the trust value of the first link over the path. The TS field has
been used to restrict a replay attack. The RH field shows the hop
value which has been formed by reducing the IH value by one in
each hop. The TR field sums up the trust value over the path.
if (Rec_W_Aging > 0.5)
begin
Rec = Rec + Rec_W_Aging
Total_No_Rec = Total_No_Rec + 1;
end
else
Rec_W_Aging
=
Rec_Wo_Aging
(1 
(( IH  RH )  1)  
)
10
*
Rec = Rec + Rec_W_Aging
Total_No_Rec = Total_No_Rec + 1;
end
if (Total_No_Rec == 0) Rec_Trust = 0;
else Rec_Trust = Rec/Total_No_Rec;
return Rec_Trust;
end
We can use the recommendation protocol from Ahamed et
al.’s [35] approach in its discreet variant and also for the
behavioral model that updates trust value upon each interaction.
3. RISKS IN TRUST FRAMEWORK:
HANDLING MALICIOUS
RECOMMENDATIONS
Sometimes a node is in a scenario where the recommendation
value contrasts the current recommendation value. We term these
values malicious recommendations, which can impact the overall
value of the node. There can be two such situations. a) When a
malicious node gives a high recommendation value for a node
when the overall value is poor. b) When a malicious node
recommends a very low value contrasting high recommendations
from others. We have adopted a statistical method to address this
issue of malicious recommendation. Our assumption is that the
number of benevolent nodes is much larger compared to the
number of malicious nodes.
This protocol is based on a statistical distribution named
Student’s t-Distribution [36, 37]. This approach is used when the
standard deviation is not known which is true for almost every
real life scenario. For example, let us take a data set of 10
recommendations which are as follows: {0.9, 0.88, 0.85, 0.75,
0.78, 0.8, 0.73, 0.2, 0.87, 0.9}. In this distribution a confidence
value of 90% indicates that the mean of the data set will be in the
interval specified by the UCL (Upper Confidence Limit) and LCL
(Lower Confidence Limit). We have used the method t-Estimate :
Mean for finding the following results for different confidence
levels.
As we can see from the table, the interval region defined by UCL
and LCL varies inversely with the confidence level. We do not
want to leave out any recommendation value unless it is an
obvious outlier. We have used a confidence level of 99% to
achieve quite a large interval region.
Table 2: Comparison of various trust based model
Model
Infrastructure
Support
Needed
Adaptiv
e
Lightweight
Dynami
c
Distri
buted
Context
specific
Behavioral
model
Risk
model
DB
A
TBA
B-trust [13]
N/A
N
Y
Y
Y
N/A
Y
N
N
Y
SSRD [25]
Y
N
Y
Y
Y
Y
N
N
N
N
PTM [26]
N/A
N
Y
Y
Y
N
L
N
N
Y
FDTM [27]
Y
Y
N
Y
Y
N
N
N
N
N
LMT [11]
Y
N
Y
Y
Y
N
L
N
N
Y
TUTC [28]
Y
N
Y
Y
Y
N
N
Y
N
Y
TMUS [12]
Y
N
Y
Y
Y
N
L
N
N
Y
TRAC [30]
N
N/A
N
Y
Y
N
N
N
N
N/A
TPAN [24]
Y
N
Y
Y
Y
N
N
N
N
N/A
TFPC [31]
N/A
N
Y
Y
Y
N
N
N
N
N
CBTM [32]
N/A
N
L
Y
Y
N
N
N
N
N
MobileTrust
[14]
Trust based
Service
sharing
model
Y
N
Y
Y
Y
Y
L
N
N
Y
Y
Y
Y
Y
Y
Y
Y
N
Y
Y
where, α = number of times a device sends a recommendation
which falls in the interval (+1σ, +2σ] or (-1σ, -2σ]
β = number of times a device sends a recommendations
which falls in the interval (+2σ, UNPL] or (-2σ, LNPL]
δ = number of times a device sends a recommendation which
has a value greater than or equal to UNPL or lower than or equal
to LNPL.
w1, w2, and w3 are weight factors with w1< w2<w3
M ean
LCL
1.2
1
0.8
0.6
0.4
0.2
In the second approach, a device will be discarded if, η >= Δ,
where, η = number of times a device sends recommendation
which falls outside of the interval defines by UCL and LCL.
Rec10
Rec9
Rec8
Rec7
Rec6
Rec5
Rec4
Rec3
Rec2
0
Rec1
Recommendation Value
Rec_Val
UCL
Recom m endation
Fig. 4. Plot result of the recommendation values using tEstimate : Mean
We have used the information of interval region for two reasons:
1. We discarded any recommendation value which is outside
this interval and took the mean of the rest of the values as the
overall recommendation value. The optimization is readily
observable. The old overall recommendation value of 0.766 has
been replaced by a much accurate overall recommendation value
of 0.828889 ((0.9+ 0.88+ 0.85+ 0.75+ 0.78+ 0.8+ 0.73+ 0.87+
0.9)/9).
2. If the recommendation value given by a specific device falls
outside the interval region for more than a specified threshold
value (Δ), the device will be discarded.
In the first approach, a device will be discarded if,
w1α
+
w2β
+
w 3δ
………………………………………………........(3)
>=
Δ
The value of Δ is dependant on the level of importance of security
the environment. In a highly sophisticated environment a very
small number of out of range recommendations will put a device
out of a network, whereas more tolerance can be shown in a less
sophisticated environment.
4. RELATED WORK
There has been a good number of works on focusing on dynamic
access control for resource sharing and interaction, as we have
listed in this section [11-14, 25-32]. A comparison table of trust
models is presented in Table 2. The following criteria are focused
on:






Risk Model
Distance Based Aging(DBA)
Time based Aging(TBA)
Lightweight
Dynamic
Distributed





Context specific
Behavioral model
Adaptability
Support of Infrastructure
While all of the models support dynamic updates of trust values,
they lack other features necessary for the dynamic pervasive
environment. Although the more recent works consider time
based aging in trust evolution, hop based aging is unique to our
model. Other models adopt complex cryptographic schemes
which are not suitable for the light-weight models. Most of them
lack a behavioral model which is most important for the evolution
of trust in such a dynamic model.
5. EVALUATION
In this section, we demonstrate two different the evaluation
criteria for our service sharing model. First we present the
implementation of the secured service sharing application
which takes advantage of the trust based access control model.
Second, we present a usability survey used to evaluate our
applixation.
5.1 Implementation
We have implemented a prototype of the secured service
sharing application with the Hop-based Trust protocol using
VC#.Net Compact Framework. Screenshots of the application
are provided in figure 5. The aforementioned application has
the ability to discover neighboring service providers and
provide a list of services available from these providers. The
following screenshots demonstrates a successful and a failed
attempt to access shared services.

How much will Context specific profiles be used? For
example, restriction to time based or location based
profile while granting the service access. The more
roles, the more the discomfort users experience. So,
we provide lowest rating for the highest number of
profiles. (Context profiles)
To what extent the user roles will be distinguished?
For uniformity of the discomfort level, the lowest
rating goes to highest number of roles. (Role extent).
Each of these characteristics was rated on scale from 1 through
5, 5 being the highest rating. Low ratings imply the user is
unwilling to share the services. The services taken into
consideration for the survey were:

Date/Time

Weather

WordPad

Chat

Unzip SW

Address book
These usability survey results show that a service’s trust based
access controls had a positive impact on the users’ willingness to
share services. After being provided with the service sharing
application, the users’ willingness to share shifted towards
positive outlook. Although sensitive services like address book
don’t show high rated shift, still the change is still visible.
Usability (Before)
5
4.5
4
Frequency
3.5
3
Access
2.5
2
Context Profile
Role Extent
1.5
1
0.5
0
Date/Time
Weather
Wordpad
Chat
softw are
Unzip SW
Address
book
(a)
Usability (After)
5
4.5
Fig. 5. Screenshots of the Service sharing application
based on trust based access control
5.2 Usability survey
We have conducted a usability survey among 20 subjects to
examine the user experience based on services offered through
our trust based access control model. The survey was conducted
in two phases. The first phase offered the users a mock up of
screens to allow the users to familiarize themselves with the
handheld device. In the second phase, the user was provided
with the actual services in order to see if their expectations were
met or exceeded. The difference shows a positive shift in the
user’s expectations (Fig. 6). The following characteristics have
been identified based on the usability survey.


How frequently the services will be shared?
(Frequency)
How many accesses per service will be provided by
default? User might consider affect of bandwidth and
quality. (Access)
4
3.5
Frequency
3
2.5
2
Access
Context Profile
1.5
1
Role Extent
0.5
0
Date/Time
Weather
Wordpad
Chat
softw are
Unzip SW
Address
book
(b)
Fig. 6. Usability survey (a) before and (b) after the service
usage
6. CONCLUSION AND FUTURE WORKS
In this paper, we presented a trust model to fit the dynamic access
control framework intended for pervasive environment. We used
this information to optimize the accuracy of the recommendation
process and the discarding of malicious devices from the network.
The prototype of the secured service sharing application presented
in the evaluation section uses this hop-based recommendation
protocol. We are integrating the behavioral model and the
dynamic update of trust with a prioritized list of services into the
application. We are also incorporating the risks involved in the
different sharing scenarios and working towards a complete risk
model.
As a continuous addendum to the features, this access control
module will be placed in the MARKS ((Middleware
Adaptability for resource Discovery, Knowledge Usability, and
Self Healing) [38] middleware. Apart from security issues in
service sharing, our future research lies with privacy issues that
may arise due to context-awareness of applications in the
pervasive environment [39].
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