Gain_Factor_Trust_A_Trust_Evaluation_Framework_in_Online_Social_Networks.pdf

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391 | International Journal of Computer Systems, ISSN-(2394-1065), Vol. 03, Issue 05, May, 2016

International Journal of Computer Systems (ISSN: 2394-1065), Volume 03 – Issue 05, May, 2016

Available at http://www.ijcsonline.com/

Gain Factor Trust - A Trust Evaluation Framework in Online Social Networks

AAnanthy E,

BPrabadevi B

AStudent, School of Information Technology and Engineering, VIT University, IndiaBAssistant Professor School of Information Technology and Engineering, VIT University, India

A

[email protected],

B

[email protected]

Abstract

In online social network (OSN), the trust evidence in the paths which is built between the immediate trustful users should

be considered to evaluate trust from one user to another user. There are some paths which may overlap with each other

leading to path dependency. A characteristic called high clustering in online social network makes the path dependency

more common. Another challenge is the decay of trust in each node while propagation. Path dependency problem and

trust decay problem is analyzed and converted in to generalized network flow problem. We propose a trust evaluation

scheme viz.,Gain Factor trust, in which we address path dependency using network flow and model decay of trust with

fixed value leakage.

Keywords: Online social network (OSN), trust evaluation, path dependency.

I. I NTRODUCTION

Trust is the most important factor in social activities.People make friends or make deals based on the trust.Without trust, there may be high risk in our daily activities.Still there remains a question on how to evaluate trust inthis computerized world where there are many chances thatwe have never met our friend in real life. In real world wehave some methods to evaluate trust, for example it may be

based on social distance. A may trust B strongly because B

is known to A for a long time. B introduces C to A, and Amay trust C based on the trust on B. But there is adifference in trust. A does no trust C as much as trusting B

because the social distance is not so close. A good trust isformed by long-time interaction and shorter distance. Trustis a very important feature in online social network.

One of the advantages of OSN is that users can makefriends with others who may be from different country orcontinent. The person may be a complete stranger. Wehave to analyze that the stranger can be trusted or not

because the trust is not only a factor for relationships between the users but also for security issues. Allowing astranger to view or access a user’s profile in OSN may also

lead to many privacy risks. Features like date of birth,gender, location can be used for identity theft, and contactinformation such as email id, messages, contact name, ormobile number can be used for any spamming activities.As fast as the OSN develop rapidly, the security and

privacy issues are attracting more attention.

Trust can be formed by direct contact or by anyrecommendation by a known person. In a trusted path (x, p,

y), p recommends x to trust y. A path is constructedthrough recommendation. So there are chances foroverlapping. In fig 1 e(x, p) and e( p, y) are two edges of asequential path ( x, p, y); ( x, p, y) and ( x, q, y) are two

parallel paths; (x, p, q, y) is an overlapped path with

paths( x, p, y) and ( x, q, y). Normally each edge has aweight value between 0 (no trust) and 1 (full trust) toquantify each direct trust.

Figure1. Trusted Graph

II. MOTIVATION

On the web, "trust" has been an issue of security and privacy. However, work has also focused on using themore social aspects of trust. Each user on the site has asingle trust values which are calculated from the

perspective of designated seeds (accepted nodes). Trustcalculations are done by using a network flow model. Themetric composes certifications between members todetermine the trust level of a person. Users can be certifiedat three levels: Close friends, friends, less-known. Accessto post and edit the information is controlled by thesecertifications. This metric is quite attack resistant. Byidentifying individual nodes that are bad and finding anynodes that seem to be the bad nodes, the metric cuts out anunreliable portion of the network. Calculations are basedmainly on the good nodes, so that the network as a wholeremains secure. Because of its use of groups to determinewho can post messages, it is called a group trust metric.Current social network systems mainly focus on trustvalues between one user to another, and thus theiraggregation function would need some changes to beapplied. Their paper does not completely define a specificmultiplication function for calculating trust betweenindividuals, but present a general framework as their mainresult. However, socially, trust is not a finite resource, it is

possible to have very high trust value for a large number of people, and that trust is not any weaker than the trust held by a person who only trusts one or two others.


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Ananthy E et al Gain Factor Trust - A Trust Evaluation Framework in Online Social Networks


III. BACKGROUND AND RELATED WORK

Several trust computing mechanisms have been proposed from various perspectives but most of them justmost of them quantify trust related factors and integrate toa trust value by setting a weight for each factor. Trustevaluation is formalized as classification problem andmachine learning approach can be used. It may result inhigh accuracy in trust classifier. But the relationship

between the trusted social network and original socialnetwork remains to be studied [1].

An OSN may provide a platform for attackers tospread infections at a large scale. Insider threats give greatthreat to the organization which may have been motivatedto steal vital data. Malware known as socio-ware exploitsOSN environments and makes it to perform unauthorizedand wicked activities. Socio-ware can be an executable, anextension, an exploit code, etc., that performs maliciousactivities. The problems associated with various classes ofsocio-ware were addressed [2].

Pre-processing a social network can be by developing asimple and practical user-domain-based trustedacquaintance chain discovery algorithm through using thesmall-world network characteristics of online socialnetworks and taking advantage of weak bonds. Building atrust network (BTN) and generation of a trusted graph(GTG) with the adjustable width can be done by using

breadth-first search algorithms [3].

Degree and Contact Interval can be taken intoconsideration, which produce a new trust evaluation model(T-OSN). It is aimed to solve how --to evaluate the trustvalue of an OSN user. It is not based on features of

traditional social network, such as, distance and shortest path. Special features of OSN to build up the model, thatincludes numbers of friends (Degree) and contactfrequency (Contact Interval). It makes more suitable toevaluate OSN users trust value. The formulations of themodel are quite simple but effective which will not cost toomuch resource and it is easy to implement for an OSNwebsite [4].

IV. PROBLEM DEFINITION

Given a trusted graph G = (V, E ),

V is the set of nodes and E is the set of edges. For twoindirectly connected nodes, x and y in V , x is the source and

y is the destination.

In this work we consider that trust value for any twoconnected users is already known and it can be represented

by numerical values i.e. from 0 to 1 where 0 represents notrust and 1 represents full trust. This is direct trusted values.Our aim is to find trust values between any unconnectedusers based on the known users. There may be leak in thenetwork flow where it is sent through.

V. SOLUTION

Gain Factor Trust

Figure2. Architectural Diagram

We set the node leakage value earlier. To do so wemust know how much leakage is there in each flow. Inreality it is not easy to find it in a complex social network.There are many factors to be considered such as distance,

strength between users. By fixing the leakage value we canavoid the decay of trust. The leakage value can be setflexibly. Here we do merging and splitting of nodes,therefore we can avoid overlapping of paths which leads to

path dependency.

Algorithm

G, a trusted graph

x, source

y, destination

1. For each intermediate node q in G do

2. Split q in toq+

andq-

and add an edge to it.

3. For each edge e in G do

4. If e is intermediate edge then

5. Capacity of e is 1

6. Gain factor is 1-leak(q)

7. Else

8. Capacity of e is trust value

9. Gain factor is 1.

This approach avoids information reuse because thecapacity value of edges will change once it is used. The

flow will be through all edges and hence information loss isavoided. The resulting values will obviously fall between 0and 1 and therefore no need to normalize the values. It alsoeliminates false positive effects. It is more generic.


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Ananthy E et al Gain Factor Trust - A Trust Evaluation Framework in Online Social Networks


VI. EXPERIMENTAL EVALUATION

Figure3. System flow.

The phases in the experiment are as follows:

Initial phase

Establishing communication

among users via Request

Trust score evaluation

Setting up of Security Level

A. Initial phase

In this pre-processing phase, all users want to registertheir details in our site. It is very important to store theirdata in secret. So users have some crucial and secretinformation, these will be stored in unreadable format to

admin. Then login to their page. They have lot offunctionalities over there.

B. Establishing communication among users via Request

This phase is mainly used to establish communicationamong users. For that, initially the user needs to find theirfriend in this site. This may be based on known member orunknown member. We can communicate with anybodythrough that request. This request will be accepted by theopponent based on their wish. They can accept or reject.

C. Trust score Evaluation

This is done based on users’ communication.Communication will fall into three categories.

1. If their communication is high, the trust score will be high and they are categorized as close friends.

2. If their conversation is normal, they are categorizedas friends.

3. If they don’t have any conversation, they arecategorized as less known.

D. Setting up of Security Level

This security level is based on those categories. Eachcategory has a certain limit. Based on that security level,

the comments will be posted on their wall. Some wordswill store as a keyword in database. If they used that wordmeans it won’t display. If a post is longer used means itwill stored in a trending areas.

Figure4. Accepting friend requests

The user can accept the friend request by choosing thecategory.

Figure5. Posting updates with priority

The user posts updates with priority of who can see theupdates.

VII. CONCLUSION

As the OSN services and applications gain popularity, the issues in trust gain more attention amongthe service consumers and providers. Consumerexperience and the quality of the service can beenhanced by improving the trust evaluation accuracy.

We make use of natural network flow to deal with pathdependency and decay of trust by fixing the nodeleakage value. The process is more general saving thenormalization process.

R EFERENCES

[1] Kang Zhao; Li Pan, "A Machine Learning Based Trust EvaluationFramework for Online Social Networks," in Trust, Security andPrivacy in Computing and Communications (TrustCom), 2014IEEE 13th International Conference on , 24-26 Sept. 2014.

[2] Sood, A.K.; Zeadally, S.; Bansal, R., "Exploiting Trust: StealthyAttacks Through Socioware and Insider Threats," in SystemsJournal, IEEE , on ,2015.

[3]

Wenjun Jiang; Guojun Wang, "SWTrust: Generating Trusted Graphfor Trust Evaluation in Online Social Networks," in Trust, Securityand Privacy in Computing and Communications (TrustCom), 2011IEEE 10th International Conference on , 2011.

[4] Ming Li; Bonti, A., "T-OSN: A Trust Evaluation Model in OnlineSocial Networks," in Embedded and Ubiquitous Computing (EUC),2011 IFIP 9th International Conference on , 24-26 Oct. 2011.

[5] J. Golbeck. Computing and applying trust in webbased socialnetworks. PhD thesis, University of Maryland,2005.

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Gain_Factor_Trust_A_Trust_Evaluation_Framework_in_Online_Social_Networks.pdf