Identification of inference attacks on private Information from Social Networks

International Journal of Research for Science Technologies & Engineering (IJRSTE)
Vol-1, Issue-2, Nov-2014, ISSN 2393-8714
Identification of Inference Attacks on Private
Information from Social Networks
6
K.Meena1
Final Year M.E-CSE
meenakc.be@gmail.com
Gnanamani College of Technology,
Namakkal, Tamilnadu (India)
Dr.P.Kuppusamy2
Professor/CSE
drpkscse@gmail.com
Gnanamani College of Technology,
Namakkal, Tamilnadu (India)
Abstract - Online social networks, like
Facebook, twitter are increasingly utilized by
many people. These networks permit users to
publish details about them and to connect to
their friends. Some of the details revealed
inside these networks are meant to be
keeping private. Yet it is possible to use
learning algorithms and methods on released
data have to predict private information,
which cause inference attacks. This paper
discovers how to launch inference attacks
using released social networking details to
predict private information’s. It then
separate three possible sanitization
algorithms that could be used in various
situations. Then, it investigates the
effectiveness of these techniques and tries to
use methods of collective inference
techniques to determine sensitive attributes
of the user data set. It shows that it can
decline the effectiveness of both the local and
relational classification algorithms by using
the sanitization methods we described.
Index Terms - Social network privacy,
inference, anonymization, detail, private
information leakage, information revelation,
information.
1. INTRODUCTION
Social networks are some kind of online
applications that allow their users to connect by
means of a variety of link types. As part of their
offerings, these networks permit people to list
informations about themselves that are
important to the nature of the network. For
instance, Facebook and twitter is general-use
social network, which means individual users
list their favorite activities, books, music,
movies and so on. On the other hand, LinkedIn
is a professional network; because of these
users specify details which are related to their
professional life (i.e., reference letters, previous
employment, educational qualification and so
on.) Because those sites collect extensive
personal details, social network application
providers have an extraordinary opportunity:
direct use of these information’s could be useful
to advertisers for direct advertising. However in
practice, privacy concerns can avoid these
efforts [1]. This inconsistency between the
desired use of details and individual privacy
presents an opportunity for privacy-preserving
on social network data mining—that is, the
detection of these information’s and
relationships from social network data without
violating privacy. Instance of privacy after data
release contain the recognition of specific
individuals in a data set consequent to its
released data to the general public or to paying
customers for a specific utilization. Possibly the
most descriptive example of this kind of privacy
breach is the AOL search on data scandal. In
2006, the AOL published the search results
from 8,50,000 users for research purposes.
However, these results had a considerable
number of “vanity” searches; searches on an
individual user’s name, social security
number/id, or address—that could be attached
back to a specific individual [2]. Private
information leakages, conversely, is connected
to details about an individual user that are not
explicitly declared, but, rather are inferred
through other data released and/ or relationships
to individuals who may state that detail.
However, it is widely available, that he is a
member of the “legalize the same sex/age
marriage.” Using this publicly accessible
information about a general group membership,
it is easily guessable what Ram’s political

7
affiliation is. It notes that this is a problem both
in live data (i.e., currently on the server) and in
any released details.
My profile It Contains “Account
Information", “Basic
Information", “Contact
Information”, “Personal
Information", “My Groups".
The wall It allows other users to post notes
in a space on one's profile.
My photos It Allows users to upload
photographs and label who is in
each one. If a friend lists me as
being in a photograph, there is a
link added from my profile to
that photograph.
My groups In this Users can form groups
with other like-minded users to
show support for a cause, use the
available message boards, or find
people with similar interests.
Table 1: Features of Facebook
This paper focuses on the problem of
private information leakage for individuals as a
direct result of their actions as being part of an
online social network. It forms an attack
scenario as follows: Suppose Facebook wishes
to release data to electronic arts for their use in
advertising games to interested people.
Conversely, once electronic arts have this data,
they wish to identify the political affiliation of
users in their data for accepted efforts. Because
they would not only use the names of those
individual users who explicitly list their
affiliation, but also through inference could find
out the affiliation of other users in their data,
this would clearly be a privacy violation of
hidden information. It explores that how the
online social network data could be used to
predict some individual personal detail that a
user is not willing to release (e.g., political
and/or religious affiliation, sexual orientation)
and search the effect of possible data
sanitization approach on preventing those
private information leakages, while allowing the
recipient of the sanitized information to do
inference on non-private details. This problem
of private information leakages could be an
essential issue in some cases. Recently, both
XYZ News [3] and the Boston Globe [4]
published reports representing that it is possible
to determine a person’s sexual orientation by
obtaining a relatively small sub graph from
Facebook that includes only each user’s gender,
the gender they are attracted in, and their friends
in that the sub graph. Predicting an individual
user’s sexual orientation or some other private
detail may seem like unimportant, but in some
cases, it may build negative repercussions (e.g.,
discrimination). For example, using the
revealed social network detail (e.g., family
history, life style habits, etc), predicting an
individual user’s likelihood of getting
Alzheimer disease for health insurance and
employment purposes could be problematic.
1.1 Our Contributions
To the best of our knowledge, this is only the
first paper that discusses the problem of
sanitizing a social network data to protect from
inference of social network data and then
inspects the effectiveness of those approaches
on real-world data sets. In order to protect
privacy, we sanitizing both details and the
underlying link structure of the sub graph. That
is, we delete some information from a user’s
profile and remove some links between their
friends. We also examine the property of
generalizing detail values to be more generic
values. We then study about the effect of these
methods have on combating possible inference
attacks and how they may be used for guiding
sanitization. It further demonstrates that this
sanitization still allows the use of other data in
the system for further works. In addition, it
gives a proper privacy definition that is
applicable to inference attacks discussed in this
paper.
1.2 Overview
The residue of this paper is organized as
follows: In Section 2, it illustrates previous
work in the area of social networks anonymities
and explains what are the ideas used in existing
models. In Section 3, it explains the real world
data sets that are used in our experiments and
discuss about the problem scenario. In Section
3.1, it describes the basics of social networking.
In Section 3.2, it defines naïve bayes

8
classification that applied in our system. In
Section 3.3, it presents definition about private
data inference. In Section 4, it describes our
system model and further discuss about
anonymity and security in social networks. In
Section 5, it concludes the present system and
recommends some feasible future work in this
area.
2. RELATED WORKS
In this paper, we handle on many areas of
research that have been seriously studied. The
area of privacy inside a social network details
encompasses a large breadth, based on how
privacy is defined. In [1], Backstrom model,
consider an attack against an anonymized
network data. In their model, the network
consists of only nodes and edges or links. Detail
values are never included. The goal of the
attacker is simply to recognize people. In
addition, their problems are very different than
the one considered in this paper because they
avoid details and do not think about the effect
of the reality of details on privacy.
Hay model. [2] and Liu[3] consider a
number of ways of anonymizing social network.
However, our works focus on inferring
informations from nodes in these networks, not
particularly identifying individuals.
Other papers have tried to infer private
informations inside social networks. In [4], He
model. Consider various ways to infer private
details via friendship links by creating a
Bayesian network from the links inside a social
network. While they crawl a social network,
Live Journal, they use imaginary attributes to
analyze their learning algorithms.
Also, compared to [4], we present
techniques that can help with choosing the most
efficient details or links that need to be removed
for protecting privacy. Finally, we discover the
effect of collective inference techniques in
possible inference attacks.
In [5], Zheleva and Getoor model propose
several methods of social graph anonymization,
focusing generally on the idea that by
anonymizing both the nodes in the group and
the link structure, that one in that way
anonymizing the graph as complete. However,
their methods all focus on anonymity in the
configuration itself. For example, through the
use of k anonymity and t-closeness, based on
the quasi-identifiers which are selected, much
of the uniqueness in the detail may be lost.
Through our method of anonymity preservation,
we maintain the full uniqueness of each node,
which allows more information in the data post
release.
In [6], Gross model, it examine specific
usage instances at Carnegie-Mellon. They also
note down potential attacks, such are nodes re-identification
or stalking, that easily accessible
details on Facebook could assist with it. They
further note that when privacy control may exist
on the user’s end of the social network site, a lot
of individuals do not take advantages of this
tool. This finding corresponds very well with
the amount of data’s that we were able to crawl
using a very simple crawler on Facebook
network. We enlarge on their work by
experimentally examining the accuracy of some
types of the demographic re-identification that
they recommend before and after sanitization.
In [7], Murat model, it explore how to
launch inference attacks using released social
networking data to predict private informations.
It then classifies three acceptable sanitization
techniques that could be applied in various
situations. Then, it explores the effectiveness of
these techniques and attempt to use methods of
collective inference to discover sensitive
attributes of the data set. This paper focuses on
the problem of private information leakage for
individuals as a direct result of their actions as
being part of an online social network.
3. PROBLEMS ON INFERENCE
Privacy information can be inferred via
social relations/links, the privacy confidentiality
problems becomes gradually more challenging
as online social network services are most
popular. Using Bayesian network approaches to
model the fundamental relations among users in
social networks, it studies the contact of prior
probabilities, influence strength, and society
openness to the inference accuracy on real
online social networks. A user can filter out
other kinds of relations between two connected
people through inference.

9
3.1 Social Network Description
It begins by relating the exact composition of
a social network for the purpose of our study.
Definition 1. A social network is generally
denoted as a graph G is V, E; where V is the set
of nodes in the graph, where each node ni
represents a individual user of the social
networks. E relate to the set of edges in the
graph G, which are the links or edges distinct in
the social network. For any one friendship
link/edge Gi,j between user pi and user pj, we
suppose that both Gi,j∈ ߝ. and Gj,i∈ ߝ. D is the
set of details from the social network.
3.2 Naive Bayes Classification
Purpose of each individual user’s political
affiliation is an exercise in graph classification.
Given a node n1 with m details and p
potential classification labels, C1; . . . ; Cp,
C1x, the probability of n1 being in class
Cx, is given by the equation
ܽݎ݃ max
ଵஸ௫ஸ௣
[ܲ(ܥݔ௜|ܦ݅ଵ ,…, ܦ݅௠)],
Where arg max1≤x≤p indicates the possible
class label that maximizes the prior equation.
On the other hand, this is complex to calculate,
since P(Cݔ௜) for any given value of x is
unknown.
3.3 Privacy Inferences
It may appear that the population of
Facebook users we have studied is, by large, or
just pragmatic about their personal privacy.
Personal informations are generously provided
and limiting privacy preferences are
suspiciously used. Due to the mixture of
personal informations disclosed in Facebook
user profiles, their visibilities, their public
linkages to the member’s actual identity, and
the scope of the network, user’s may put
themselves at risk for a mixture of attacks on
their physical and online persona. Some of these
risks are general also in other online social
networks, though some are exact to the
Facebook.
4. SYSTEM MODEL
A social network is a website on
the Internet that brings people together in a
central position to chat, share thoughts and
happiness, or build new friends. This type of
teamwork and sharing of detail is often referred
to as social media’s.
Data Gathering
Data Classification
Choosing the
sensitive information
User Detail Link detail
Apply security
check
Check
validity
Show the sensitive
detail
Hide the
sensitive detail
Fig 4.1: System Architecture
In contrast conventional media that is
frequently created by no more than 15 people,
social media sites include contents that has been
created by hundreds or even millions of various
people. The following are some of the major
social networks used today.

10
 Bebo (http://www.bebo.com/) - A popular
social networking site where users can
share photo, journals, and more with their
friends and family privately or publicly on
the Internet.
 Classmates (http://www.classmates.com/)-
One of the leading and most used websites
that brings together and allows people
who graduated from high school and
allows you to keep in touch with them and
any future reunions.
 Facebook (http://www.facebook.com/) -
One of the most well-liked social network
websites. Facebook is a most popular
intention for users to setup their own
private web pages, connect with their
friends, share photos, share cinemas,
speak about what is our duties, etc.
Users who prefer to engage in social networks
without revealing their true identity will create
profiles using a false name as well as a false
email addresses. If you believe a
pseudonymous profile, submit to the terms of
services for the social network site. Providing
fake or incomplete informations violates the
terms of service of some social networking
sites. It is achievable to release identifying
information through status updates/group
memberships.
4.1 Anonymity on Social Networks
Many users of social networks choose to
mask their real identities. This may be done via
anonymity (providing no name at all) or
pseudonymity (providing a false name). Some
people who may prefer an anonymous, but not
controlled to:
 people with medical circumstances who
needs to discuss symptoms and treatment
without creating a public record of their
condition
 Bloggers and activists engaging in
political discussion, particularly on
notorious issues
 Teachers and childcare workers
 Medical professionals, including mental
health professionals
 Victims of stalking and marital violence
 Children and youth
 Jobseekers
Anonymity is a useful tool for anyone who
prefers to keep a strict separation between an
online persona and an off-line individuality. It
can also be harmed by individuals demanding to
shield their identities while doing illegal
activities.
4.1 Security in Social Networks
Online social networks, such as Face book,
are increasingly utilized by lots of people.
These networks permit users to publish
informations about them and to connect to their
friends and colleagues. Information theft
provides the jumping ground for a malicious
user to mount more attacks. Once the Hacker
has these details, he/she is free to keep in
phishing, identity hijacking and some other
forms of social attacks. These details itself is
also very valuable in the eyes of advertisers.
Some suggestions for ensuring security on
social networks are,
 Personal privacy setting
 Browsing scope setting
 Owner’s confirmation
Personal Privacy Settings, Not only should
users have greater control in assigning viewing
privileges of personal information, the method
should be more user friendly and updated. Even
though Facebook provides modifiable groups
and group-based authorization control. A
standardized model that group newly added
friends to different pre-made privilege buckets
would greatly enhance this process.
Browsing Scope Settings, The ability of users
to view information across vast spans within a
group should be restricted. For example,
detailed data should only be viewable up to a
number of quantities away. LinkedIn offers
good control in this group as a default one,
requiring authentication for anybody that is not
a direct link between them. Even though this
does not protect any person from hijacking a
profile and gaining access through other means
of social engineering, it does present an
excellent starting ground to prevent widespread
automated information harvesting.

11
Owner's Confirmation, Confirmation is a kind
of acknowledgement to the destination or
sender. Whereas much information is leaked not
through the original profile but from innocent
witness remarks, users should be specified the
authority to edit or control comments. Most
people when asked will agree that not everyone
they know is their best friend; there are the
mere acquaintances all the way to those with
whom we share our genuine secrets, along with
many shades in between them. While social
networks may not essentially improve strong
attachments, it definitely does very little for
weak attachments. The need for keeping
information secret arises from the use of
computers in sensitive fields such as
government and industry. Social networking
sites grants a certain level of access control, but
the majority of people do not take the effort to
configure these properly.
5. CONCLUSION
This system extends the existing definitions
and also removed the drawbacks with that
system and introduced a secure network that
will protect or keeps the user information more
secure. And also it will remove the
impersonation behaviors. It also containing very
secure messaging module that protects the
user’s message from other persons in the
network. It addresses various issues related to
private information leakages in social networks.
It shows that applying both friendship links and
details jointly gives better predictability than
details alone. In addition, it discovers the effect
of removing details and links in preventing
sensitive information leakage. In the process, It
expose situations in which collective
internecine does not recover on using a simple
local classification method to identify nodes.
Future work could be carried out in
identifying key nodes of the graph structures to
observe if removing or altering these nodes can
decrease information leakage and ensuring the
links between people are more secure. Effective
research could be done on how individuals with
limited access to the network could choose
which details to hide.
6. REFERENCES
[1] L. Backstrom, C. Dwork, and J.Kleinberg,
“Wherefore Art Thou r3579x?: Anonymized
Social Networks, Hidden Patterns, and
Structural Steganography”. Proc. 16th Int’l
Conf. World Wide Web (WWW ’07), pp.
181-190, 2007.
[2] M. Hay, G. Miklau, D. Jensen, P.Weis, and
S. Srivastava, “Anonymizing Social
Networks”. Technical Report 07-19, Univ.
of Massachusetts Amherst, 2007.
[3] K. Liu and E. Terzi, “Towards Identity
Anonymization on Graphs”. Proc. ACM
SIGMOD Int’l Conf. Management of Data
(SIGMOD’08), pp. 93-106, 2008.
[4] He, W. Chu, and V. Liu, “Inferring Privacy
Information from Social Networks”. Proc.
Intelligence and Security Informatics, 2006.
[5] E. Zheleva and L. Getoor, “Preserving the
Privacy of Sensitive Relationships in Graph
Data”. Proc. First ACM SIGKDD Int’l
Conf. Privacy, Security, and Trust in KDD,
pp. 153-171, 2008.
[6] R. Gross, A. Acquisti, and J.H. Heinz,
“Information Revelation and Privacy in
Online Social Networks”. Proc. ACM
Workshop Privacy in the Electronic Soc.
(WPES ’05), 2005.
[7] Raymond Heatherly, Murat Kantarcioglu,
and Bhavani Thuraisingham, “Preventing
Private Information Inference Attacks on
Social Networks”. Proc. IEEE Computer
Society, 2013.
[8] N. Talukder, M. Ouzzani, A.K.
Elmagarmid, H. Elmeleegy, and M. Yakout,
“Privometer: Privacy Protection in Social
Networks”. Proc. IEEE 26th Int’l Conf.
Data Eng. Workshops (ICDE ’10), pp. 266-
269, 2010.
[9] K.Periyasamy and K.Nagendran,
“Preventing Private Information Leakage
form Social Networks”. Proc. International
Journal of Advance Research in Science and
Engineering, 2014.
[10] Mislove, A., Viswanath, B.Gummadi and
K.P. Druschel, “You are who you know:
Inferring user profiles in online social
networks”. Proc. WSDM, (2010).

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