International Journal of Engineering Trends and Technology (IJETT) - Volume 35 Number 4 - May 2016
Evaluation of Trust in Facebook
using GFTrust Algorithm
Saket Kumar*, Shreya Shripathi*, Sanjukta Phukan*, Mrs. Ashwini K**
*Department of ISE, SJB Institute of Technology, Kengeri, Bengaluru- 560060
**Asst Professor, Department of ISE, SJB Insititute of Technology, Kengeri, Bengaluru- 560060
Abstract— The immense growth Facebook as one of the most
used Online Social Networks (OSNs) has made it to play a major
role in connecting people together through OSNs. But the role of
Trust in connecting people has become a major challenge and
issue in present world. Since Nowdays the probability of
malicious behaviour in OSNs has increased hence it is difficult to
Trust anyone on Facebook . Hence, it is desirable to find out how
much a person should trust one another in Facebook . The
approach to answer this question is usually called trust inference
through Trust Score. In this paper, we propose a new trust
inference algorithm (Called GFTrust) based on the Genralized
network flow concept. The algorithm, in addition to being simple,
resolves some problems of Trust Evaluation in Facebook . The
analysis of the algorithm demonstrates that GFTrust calculates
the trust score which defines the degree of Trust for any given
individual on Facebook.
Keywords—generalized network flow, online social networks,
path dependence, trust decay, trust evaluation
I. INTRODUCTION
In recent years, online social networks (OSNs) like Facebook,
LinkedIn, and Twitter have become fundamental parts of our
online lives, and their popularity is increasing day by day.
Among the social networking sites, Facebook— with nearly
1.28 billion users worldwide as of the first quarter of 2014—
has been one of the most successful ones in recent years when
compared with other online social networks. Facebook
nowadays is being used as a tool to connect with family,
friends, colleagues, associates, known or unknown people
based on the same interests or for different purposes such as
professional works, advertising their brands and businesses,
making profit or entertainment.
Besides the revolution that Facebook has generated in
online social networking, it has also introduced new threats to
its users, because of the number of increasing users on
Facebook as people share their ideas, views along with their
images and personal informations (phone number, relationship
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status etc).Often users also use Facebook for chatting and
texting one another for interacting between different users,
that is when the Trust term comes into picture, as often we
cannot trust to as to the user we are interacting with on the
Facebook is authentic or not. Being a part of users’ daily lives,
Facebook introduce new security concerns especially because
of the potential exposure of huge amounts of personal
information that the users share on Facebook. Some examples
of threats to Facebook include creation of fake profiles, false
facebook pages to get password and username, spamming etc .
As a result, maintaining security and privacy of Facebook
profiles by users has become a major challenge in recent
years.
“Trust in a person is a commitment to an action, based on a
belief that the future actions of that person will lead to a good
outcome (Goelback [2]).” In this paper, we mainly discuss the
GFTrust Algorithm and how it can be implemented to
calculate the trust score for any given individual in Facebook
so as to gain either full Trust or no Trust on them.
Additionally, we also show how users can protect themselves
from any sort of unknown and untrusted users. Finally, based
on the parameters of Facebook we use use our algorithm to
show how a Trust Score is generated for a given individual so
as to make sure the user can be trusted or not.
The rest of the paper is organized as follows: Section II,
gives the background, Section III discusses the concept of
GFTrust algorithm and details of the algorithm. Section IV
will discuss system architecture and module description.
Section V gives proposed system. Section VI gives the module
description. Lastly, Section VII will give us the conclusions &
future work .
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International Journal of Engineering Trends and Technology (IJETT) - Volume 35 Number 4 - May 2016
II. BACKGROUND
In this section, we will first we first analyze the necessity of
defining the two challenges that are path dependence and trust
decay and then discuss about the trust evaluation.
A. Path Dependence
Path Dependence is a term that shows how a set of
decisions a user faces for any given situations becomes limited
by the decisions that the user had made in past, even though the
user’s past circumstances are no longer relevant. Once a
society becomes use to a given circumstance it is tough to
change from that circumstance. For example if a person is used
to of writing letters it is tough for him to send emails as he is
used to and dependent upon writing letters as his path
dependence makes it difficult for him to use the new
technology.
B. Trust Decay
The term Trust Decay means the decay or loss in the
amount of trust from one user to another there are several
factors for the trust decay like distance from source, the tie
strength between users and personality of users (some people
may opt to trust others, while some others may opt to disrupt).
Here we mainly consider the distance as a factor for the
trust decay as more the distance can cause loss and leakage of
information so the quality of service may also reduce because
the distance will be more so it may lead to larger time taking
and the data or information form one user to another user may
reduce in the quality so there is a loss in quality of service
when it comes to distance a s a factor and hence there is a
decay in the trust from one user to other.
B.1. Trust Evaluation
The term Trust Evaluation means the evaluating of trust
form one user to another user in Facebook, based on the
parameters like comments, interactions and tags. Each user
is evaluated based on the given parameters and a trust score
is generated for the given user based on that score the user
is categorised as being trusted or not.
there is no flow send ; this situation shows no matter how good
be the recommendation from intermediate nodes be s will
never trust d . Finally , if fo = ∞, that means there is an infinite
flow to be sent from s to d only with the condition that there
should be enough paths from s to d, in this case s will trust d
highly since it is a case of heavy flow.
In this paper however we do not consider the case of fo=∞,
we just consider the case of 0<f0<1, since it is more flexible
and the node leakage will take the same role. That is, we can
set a larger node leakage if fo is larger, and vice versa.
B. Exploring the Leakage at nodes
Here we actually set the node leakage associated to
each nodes, because the trust decay is caused by nodes rather
than the edges. Generally the node leakage may be caused by
several factors like the type of users and distance from the
users, while modeling however we need to take care of certain
factors like, “amount of flow that leak in each node”. The
GFTrust algorithm provides scheme to a framework for
considering these factors for future.
In our study we consider proportional leakage as
often during the propagation from one user to other user the
trust (flow) will shrink for every given intermediate node.
There is also a possibility of fixed leakage where the trust
(flow) is lost in a certain fixed amount.
C. Assiging Capacity to Edges
Here we actually assign the capacity to each and
every edge based on its capacity, we use the Trust Value t(e)
on the given edges to represent the capacity of the given edge.
Using the value the Trust value cannot be overused and will
be in the given bounded limit, also in case when there are
more number of paths existing.
Hence on basis of the above given analysis the
GFTrust algorithm works as :1.
2.
3.
Modelling trust decay with node leakage.
Constructing generalized flow network.
Calculating a near – optimal generalized flow.
III. GFTRUST ALGORITHM DETAILS
In this section, we introduce the details about the GFTrust
algorithm, there are already several works done on trust
evidence collection (e.g ., Golbeck [2], Massa and Avesani
[27]), so we do not have to look for collecting information, we
assume that there is a graph with the trust relationships and
values of directly-connected neighbours are already available.
A.
Initial Flow Determination
As discussed earlier about the problems hence considering
those in mind we use the concept and assume the initial trust ,
we let the initial flow from s be f =1 as it is the most natural
value of initial flow. However if the value of fo= 0 that means
figure 1. Algorithm for GFNear Optimal
0
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International Journal of Engineering Trends and Technology (IJETT) - Volume 35 Number 4 - May 2016
IV. SYSTEM ARCHITECTURE
Based on the above understandings we implement the
GFTrust algorithm for Facebook and for that we use the
architecture in (Fig. 2.) The system architecture helps us to
understand better about the use and working of the GFTrust
algorithm in the Facebook to calculate the Trust Score and to
evaluate the Trust for the given person on facebook the range
of Trust score is from 0 to 1 based on the category we place
them. Generally the process starts with the request sender
sending friend request to user and the user does the
authorization of the request sender .
c)Less Known: In this category we place the users to
whom we don’t know actually but due to the number of
mutual friends we accept them as our friends so we place such
users under the less known category and the Trust Score
assigned to them is 0.25 .
B. Context Information
The context information deals with the number of
friends (mutual friends) request sender has with the user and
the dynamicity of the request sender. The dynamicity involves
the following based on which the user is classified as trusted
or untrsuted :1. number of login counts of request sender .
2. number of posts, comments, uploads
request sender.
3. active and inactive status request sender .
The above parameters are used to decide how many times
the request sender logs in to their profile, how many times they
posts picture or comments and uploads their details on their
profile and what was the time duration upto which the request
sender was active from their profile. These parameters are
taken in consideration to decide the authenticity of the given
request sender on Facebook .
C. Interaction
The Interaction tells the number of times the request sender
interacted with the user based on the frequency of the messages
sent on messenger, and the number of times the request sender
commented to the user and tagged him in comments to show
the frequency of interaction between the request sender and the
user .
V. PROPOSED SYSTEM
figure :- 2. System Architecture
The entire architecture is divided into three mainfolds :1.
Experience (Ex) .
2.
Context Information (CI) .
3.
Inetraction (I) .
A. Experience
The experience deals with the understanding about a
person when we receive a friend request from a request sender
on facebook , we often place the request sender into the below
mentioned three categories :a) Close Friends: Close Friends are the ones to whom
we give full trust as we know them very well hence we place
them to category of close friends and that gives them a Trust
Score of 1 .
b) Friends: Friends are the ones to whom we are not so
close but we know them so when we place a user in the
friends category the trust score given to them is 0.75.
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We propose the GFTrust scheme,where we use a
modified generalized network flow model for the purpose of
trust evaluation and to generate Trust Score regardless the
problem of path dependence and trust decay.There is a given
threshold between the nodes u and v once that threshold is
achieved, v is taken as trusted by u, but with a given limit on
capacity. Here we consider 1 to be the measure for full trust,
corresponding to a full trust. Our contributions are threefold :( 1) Our work is the first to address the two challenges of path
dependence and trust decay simultaneously, in the domain of
trust evaluation in Facebook. Also, we use a modified
generalized flow model with leakage, which is a novel
approach in trust evaluation.
(2) As a flow-based model, GFTrust has the advantage of
generality, while saving the normalization process. Moreover,
it bears the properties of incentive compatibility and
Sybiltolerance, and it coincides with the basic axioms that a
trust model should meet.
(3) We conduct extensive experiments on Facebook based on
GFTrust, to actually calculate the Trust Score of any given
user on Facebook, to prove the Authenticity of the user.
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International Journal of Engineering Trends and Technology (IJETT) - Volume 35 Number 4 - May 2016
VI. MODULE DESCRIPTION
In this section, we discuss step by step of how
exactly the GFTrust will work on the Facebook and the Trust
Score is calculated for any given individual , we also see how
the security level is set for protection of users ,
into friends. If they don’t have any conversation means they
are underlying the less known category.
4.Security Level:
This security level is based on those categories. Each has a
certain limit. Based on that security level, the comments will
be posted on their wall. Some words will store as a keyword in
database. If they used that word means it won’t display. If a
post is longer used means it will stored in a trending areas.
VII.CONCLUSION AND FUTURE WORK
figure 3. Use case diagram
The following are the steps that are actually used to complete
the entire process in Facebook Trust Score generation :1. Preprocessing:
In this preprocessing phase, all users want to register their
details in our site. It is very important to store their data in
secret. So users have some crucial and secret information,
these will be stored in unreadable format to admin. Then login
to their page. They have lot of functionalities over there.
Facebook has increased disclosure of personal
information by making more information available online.
Despite all the proactive security monitoring technologies that
are used by Facebook nowadays, there are still chances of
people getting cheated by fake profiles and sometimes cyber
attacks and information disclosure.
In this paper, we have reviewed the most common
problem on Facebook about gaining Trust over a particular
individual, using the proposed GFTrust algorithm. We have
also discussed some counter measures that can be used against
the problem to cure untrusted profiles on Facebook in order to
maintain privacy, integrity, and availability of data that is
being shared by the users in their Facebook profiles.
However, taking everything into consideration, users have
a critical role in protecting their own information by adhering
to a set of security guidelines. Indeed, users themselves are
responsible for any content that they share in their profiles,
ignoring the fact that malicious users may find a way to access
such content and initiate undesirable activities against them.
However there was leakage associated with the distance,
and a type of proportion leakage. In future work, we will
improve the design, and also try the approach to fix the value
of leakage.
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