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P2P Appliance Calculation Method For Trust between Nodes
within a P2P Network
Adis Medic1, Adis Golubovic2
1 InfoSys LTD,
Kolodvorska bb, 77240 Bosanska Krupa, Una Sana Canton, Bosnia and Herzegovina
Ceravačka brda 106, 77000 Bihać, Una Sana Canton, Bosna and Herzegovina
2 Pimary School „Podzvizd“, Velika Kladusa, Bosnia and Herzegovina,
Abstract
Modern ways of communications, such as in a web services
environment, also influences trust relationships between
organisations. This concept of web-based (way towards semantic
web services) trust is new and has as yet not been resolved. We
hope that some of the trust properties mentioned above can be
successfully employed to improve the understanding of trust
between machines. So, trust is a vital ingredient of any successful
interaction between individuals, among organizations and/or in
society at large. In this paper, we suggested a trust model using
fuzzy logic in semantic network of nodes. Trust is an aggregation
of consensus given a set of past interaction among nodes
(semantic network based on machines, agents etc.). We applied
our suggested model to semantic networks in order to show how
trust mechanisms are involved in communicating algorithm to
choose the proper path from source to destination. Authors use
the terms untrust and distrust as synonyms for the condition
opposite to the trust.
Keywords: Trust; Untrust; Fuzzy model; packet; node; path.
1. Introduction
Trust relationships between organisations are, among
others, influenced by culture and adherence to codes of
best practices. A model of inter-organisational trust
illustrates that trust is dependent on: competence,
consistent positive behaviours and goodwill [14]. Trust is
a central component of the Semantic Web vision. The
Semantic Web stack [3][4][10] has included all along a
trust layer to assimilate the ontology, rules, logic, and
proof layers. Trust often refers to mechanisms to verify
that the source of information is really who the source
claims to be. Signatures and encryption mechanisms
should allow any consumer of information to check the
sources of that information. In addition, proofs should
provide a tractable way to verify that a claim is valid. In
this sense, any information provider should be able to
supply upon request a proof that can be easily checked that
certifies the origins of the information, rather than expect
consumers to have to generate those proofs themselves
through a computationally expensive process. The web
the web a unique source of information, but we need to be
able to understand where we are placing our trust
[1)][2][3][4].
Trust plays a central role in many aspects of computing,
especially those related to network use. Whether
downloading and installing software, buying a product
from a web site, or just surfing the Web, an individual is
faced with trust issues. Does this piece of software really
do what it says it does? Trust has another important role in
the Semantic Web, as agents and automated reasoners
need to make trust judgements when alternative sources of
information are available [8]. Computers will have the
challenge to make judgements in light of the varying
uncensored) sources offer. Today, web users make
judgments routinely about which sources to rely on since
there are often numerous sources relevant to a given query,
ranging from institutional to personal, from government to
private citizen, from objective report to editorial opinion,
etc. These trust judgements are made by humans based on
reputation, or past personal experience about its quality
relative to other alternative sources they may consider.
Humans also bring to bear vast amounts of knowledge
about the world they live in and the humans that populate
the web with information about it. In more formal settings,
such as e-commerce and e-science, similar judgments are
also made with respect to publicly available data and
services. All of these important trust judgments are
currently in the hands of humans. This will not be possible
in the Semantic Web, where humans will not be the only
IJCSI International Journal of Computer Science Issues, Vol. 9, Issue 4, No 2, July 2012
ISSN (Online): 1694-0814
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125
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consumers of information. Agents will need to
automatically make trust judgments to choose a service or
information source while performing a task [6].
Reasoners will need to judge which of the many
information sources available, at times contradicting one
another, are more adequate for answering a question. In a
Semantic Web where content will be reflected in
ontologies and axioms, how will a computer decide what
sources to trust when they offer contradictory information?
What mechanisms will enable agents and reasoners to
make trust judgments in the Semantic Web? Trust is not a
new research topic in computer science, spanning areas as
diverse as security and access control in computer
networks, reliability in distributed systems, game theory
and agent systems, and policies for decision making under
uncertainty. The concept of trust in these different
communities varies in how it is represented, computed,
and used. While trust in the Semantic Web presents unique
challenges [13], prior work in these areas is relevant and
should be the basis for future research.
Trust can be viewed at a micro or macro level. At the
micro level, a series of tactics can, in various
circumstances, help create or preserve trust. At the macro
level, such tactics need to be combined into trust
strategies. Various tactics were set out, some of which are
variants on others. For example, there are many variations
on the tactic of restricting those sources of knowledge that
a knowledge technology uses, including relying on
branded websites, and demanding verifiable certification
of provenance. Managing trust is a key managerial
requirement for the semantic web, and an interesting
demand that has come to light is for informative metadata
about knowledge sources that can be used for assessing
trustworthiness [11].
2. Modeling a Fuzzy and Mathematical model
While a lot of concepts as well as practical applications for
the lower layers (Fig. 1) of the Semantic Web exist and
of e-commerce use-cases, the top-
has still been far away from implementations during the
last years.
Fig. 1. Stack for the semantic web [7][10]
Within this approach a link to define trustful Semantic-
Web-data of a company is integrated. Similar projects for
private usage map this approach to the area of social
networking platforms like Facebook
1
. The basic idea is to
provide an easy method for web users to indicate data
within the web as trustful, so that intelligent web
applications can work with this information without any
further trust proof mechanisms like digital signatures[15].
Trust is one of the major problems for the success of
computer supported society, smart physical environment,
virtual reality, virtual organization, computer mediated
interaction etc. It seems important to study people's trust in
the computational infrastructure, people's trust in potential
partners, information sources, data, mediating agents,
personal assistants and agents' trust in other agents and
processes. Trust is indeed a problem: for example, in e-
commerce it is far from obvious whether existing paper-
based techniques for fraud detection and prevention are
adequate to establish trust in an electronic network
environment, where you usually never meet your trade
partner face to face, and where messages can be read or
copied a million times without leaving any trace.
Of course, the notion of trust is also important in other
domains of agents' theory, beyond that of electronic
commerce. For example, trust is relevant in human-
computer interaction, e.g. the trust relation between the
user and his personal assistant (and, in general, his
computer). It is also critical for modeling and supporting
groups and teams, organizations, co-ordination,
negotiation through computational devices, with the
related trade-off between local/individual utility and
global/collective interest; or even in modeling distributed
knowledge and its circulation.
1
See http://opentrust-project.com for details.
IJCSI International Journal of Computer Science Issues, Vol. 9, Issue 4, No 2, July 2012
ISSN (Online): 1694-0814
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126
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In conclusion, the notion of trust is crucial for all the major
topics of Multi-Agent systems. In all these contexts,
different kind of trust are needed and should be modeled
and supported:
trust in the environment and infrastructure (the socio-
technical system);
trust in your agent and in mediating agents;
trust in the potential partners;
Trust in the warrantors and authorities (if any)
1
.
The problem is therefore how to build trust in users and
agents and how to maintain it. Security measures are not
enough, interactivity and knowledge are not enough.
Building trust in fact is not just a matter of protocols,
architectures, mind-design, clear rules and constraints,
controls and guaranties. Trust in part is a socially emergent
phenomenon; it is a mental stuff yet it is grounded in
socially situated agents and it is based on social context.
In this paper authors will show how to calculate a path(s)
and trustfulness of nodes inside the paths. These paths are
channels for data interchange among and inside various
networks and intelligent systems or Semantic web
repositories [16]. Also, it will be presented mathematical
and fuzzy model and formulae for trust factor calculation,
explained on examples. First of all, authors will explain an
situation that worked for analysis and show primitive and
simplified path-route modeling started scratch, also it will
be shown a step-by-step calculation for path trust factor
and confirmation of these calculation by proposed
mathematical model.
Destination
Source
1
2
3
4
5
6
7
8
9
10
11
Fig. 2. P2P network model and possible packet routes
Fig. 2 shows the initial model of tested network on which
to perform research of confidentiality between the nodes.
Values for Trust/Untrust[9] are assumed values. In this
paper authors will deal with the method of measurement,
but authors propose a new method for the most
confidential way (for packet or query results or some other
traffic) through the network if we are familiar with the
value. On next few figures authors will present values for
1
See http://www.istc.cnr.it/T3/ for details
individual nodes, trust and distrust between the nodes in
the network:
[0,7 0,3]
[0,2 0,8]
[0,8 0,2]
1
2
3
4
5
6
7
2
3
4
5
6
7
1
[0,4 0,6]
[0,4 0,6]
[0,7 0,3]
2
3
4
5
6
7
1
[0,75 0,25]
[0,6 0,4]
[0,6 0,4]
2
3
4
5
6
7
1
[0,8 0,2]
[0,6 0,4]
[0,8 0,2]
[0,8 0,2]
[0,5 0,5]
[0,95 0,05]
[0,3 0,7]
Source
[0,8 0,2]
[0,5 0,5]
[0,95 0,05]
[0,3 0,7]
Source
[0,8 0,2]
[0,5 0,5]
[0,95 0,05]
[0,3 0,7]
Source
[0,8 0,2]
[0,5 0,5]
[0,95 0,05]
[0,3 0,7]
Source
A – Confidentiality between nodes
S – 1 – 5, S – 1 – 6, S – 1 – 7 B – Confidentiality between nodes
S – 2 – 5, S – 2 – 6, S – 2 – 7
C – Confidentiality between nodes
S – 3 – 5, S – 3 – 6, S – 3 – 7 D – Confidentiality between nodes
S – 4 – 5, S – 4 – 6, S – 4 – 7
Fig. 3. Assumed Trust/Untrust values between nodes (from Source)
[0,9 0,1]
[0,8 0,2]
[0,1 0,9]
[0,3 0,7]
[0,2 0,8]
[0,3 0,7]
[0,6 0,4]
[0,8 0,2]
Destination
Source
1
2
3
4
5
6
7
8
9
10
11
[0,3 0,7]
[0,90 0,1]
[0,4 0,6]
[0,3 0,7]
[0,2 0,8]
[0,3 0,7]
[0,6 0,4]
[0,8 0,2]
Destination
Source
1
2
3
4
5
6
7
8
9
10
11
[0,6 0,4]
[0,6 0,4]
[0,7 0,3]
[0,9 0,1]
[0,2 0,8]
[0,3 0,7]
[0,6 0,4]
[0,8 0,2]
Destination
Source
1
2
3
4
5
6
7
8
9
10
11
A – Confidentiality between nodes 5 – 8 – D, 5 – 9 – D, 5 – 10 – D, 5 – 11 – D
B – Confidentiality between nodes 6 – 8 – D, 6 – 9 – D, 6 – 10 – D, 6 – 11 – D
C - Confidentiality between nodes 7 – 8 – D, 7 – 9 – D, 7 – 10 – D, 7 – 11 – D
Fig. 4. Assumed Trust/Untrust values between nodes (to
Destination)
IJCSI International Journal of Computer Science Issues, Vol. 9, Issue 4, No 2, July 2012
ISSN (Online): 1694-0814
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Table 1. Trust Factor Scale
Trusted
Untrusted
1
0
Very high (VH)
0,85
0,15
0,70
0,30
High (H)
0,50
0,50
Indifferent (I)
0,30
0,70
Low (L)
0
1
Very low (VL)
Table 2. Possible Packet Route (from Figure 2)
P1 =
P13 =
P25 =
P37 =
P2 =
P14 =
P26 =
P38 =
P3 =
P15 =
P27 =
P39 =
P4 =
P16 =
P28 =
P40 =
P5 =
P17 =
P29 =
P41 =
P6 =
P18 =
P30 =
P42 =
P7 =
P19 =
P31 =
P43 =
P8 =
P20 =
P32 =
P44 =
P9 =
P21 =
P33 =
P45 =
P10 =
P22 =
P34 =
P46 =
P11 =
P23 =
P35 =
P47 =
P12 =
P24 =
P36 =
P48 =
Trust factor is presented by formula:
F = [
T U
], (1)
Where:
T means Trust factor, and
U is Untrust factor (nonT), so we have the following:
- The values of confidentiality are possible in the
following intervals:
(2)
the extreme factor values
F
max
= [1,0] or F
max
= [100%, 0%], (3)
For the highest Trust value (or the lowest Untrust value)
F
min
= [0,1] or F
min
= [0%, 100%], (4)
For the highest Untrust value (or the lowest Trust
value).
Maximum confidentiality has an initial node S,
F = [1,0] (5)
(6)
The Trust factor is analyzed by following logic-algorithm
assumptions:
If
then, Node
n+1
is acceptable (7)
If
then Node
n+1
is not acceptable (8)
Else,
Node
n+1
indifferent acceptance (9)
3. Calculation of paths and path table
Confidentiality testing for nodes in a P36 path (Test of
confidentiality nodes in a path that represents the most
likely route for packets from source to the destination
Figure 5):
[0,95 0,05]
[0,6 0,4]
[0,9 0,1]
[0,8 0,2]
Destination
Source
3
7
11
Fig. 5. The most likely packet route, from S(ource) to D(estination)
(10)
(11)
(12)
(13)
Testing untrust of nodes in a path P36 (Test for
untrust of nodes in a path that represents the most likely
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ISSN (Online): 1694-0814
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route for packets from source to the destination Figure
5):
:
(14)
(15)
(16)
(17)
The highest Trust factor is on following paths:
P12 (18)
P36 (19)
The lowest Untrust factor, shows that this path has the
highest Trust factor. And here are the most reliable paths:
P12 (20)
P36 (21)
[0,8 0,2]
[0,95 0,05]
[0,8 0,2]
[0,6 0,4]
[0,9 0,1]
[0,8 0,2]
Destination
Source
1
2
3
4
5
6
7
8
9
10
11
Fig. 6. The most likely packet route through the P2P network
Important (very high) Trust value for paths:
P36 (22)
P12 (23)
Also, important (very small) Trust value (or very high
Untrust value) for paths (it automatically presents a
calculation check for previous results for Trust value):
P36 (24)
P12 (25)
By analyzing previous calculation, we can conclude that
the highest total confidentiality is the path P12, but most
likely route packets will take place, is P36 path because
node S has the highest confidentiality towards third node.
4. Conclusion and further work
Note that during the analysis was used mesh topology,
and we consistently follow the P2P communication
appliance [12] among nodes within the network. Also, the
authors approved a proposed way of calculating the
importance of certain nodes within the path, which is
considered to be acceptable for realization of
communication and exchange of knowledge or
information and in order to achieve results.
In future research, it will be very important, also
challenging, to establish accurate and relevant metrics
model. It is necessary to try to optimize either combination
of different metrics to get meta-model for suitable metrics
[12][5] for use or adapted for use in the previously shown
method. The authors will, in future work, try to propose a
fuzzy routing table with some interesting factors which
directly involve with confidentiality of trusted nodes, such
are factor for node that has unreliable neighbor nodes,
factor for node that is trusted but not used for packet
transfer, or factor for node that transferred a packet. By
using fuzzy logic to determine the weights for direct trust
as well as reputation, our fuzzy trust model becomes
flexible to rely on direct trust or on reputation based trust.
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IJCSI International Journal of Computer Science Issues, Vol. 9, Issue 4, No 2, July 2012
ISSN (Online): 1694-0814
www.IJCSI.org
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Copyright (c) 2012 International Journal of Computer Science Issues. All Rights Reserved.
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Adis Medić received his masters degree (Dipl.-Ing. EE-Inf/
MScEE-Inf; 2008) in Electrotechnics – Informatics from the
Technical Faculty of Bihać, University of Bihać. He is a PhD
student on Faculty of Organization and Informatics, University of
Zagreb. He is currently employed at Infosys ltd as a system and
network engineer.
Adis Golubović received his masters degree (Dipl.-Ing. EE-Inf/
MScEE-Inf; 2009) in Electrotechnics – Informatics from the
Technical Faculty of Bihać, University of Bihać. He is a PhD
student on Faculty of Organization and Informatics, University of
Zagreb. He is teaching Informatics at local primary school.
IJCSI International Journal of Computer Science Issues, Vol. 9, Issue 4, No 2, July 2012
ISSN (Online): 1694-0814
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