A Survey on Privacy in Social Networking Websites

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 03 Issue: 01 | Jan-2016 www.irjet.net p-ISSN: 2395-0072
© 2016, IRJET ISO 9001:2008 Certified Journal Page 395
A SURVEY ON PRIVACY IN SOCIAL NETWORKING WEBSITES
Dr. N. Jayalakshmi1, R.G. Kavitha2.
1 Professor, Dept. of Computer Applications, Saveetha Engineering College, Chennai, India.
2 Assistant Professor, Dept. of Computer Applications, RNS First Grade College, Bangalore, India.
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Abstract - Most Social Networking Websites
facilitate users with the features like online
interaction, sharing of information and developing
new relationships etc. As a result, more and more
data containing personal information such as
name, gender, age, birthday, email address, phone
number, current university/company, political
views and so on are published over the Internet,
which might be lead to a serious privacy leakage.
Since some of these items are sensitive, access
control is widely employed in order to protect the
privacy of users. The current protection
mechanisms, such as privacy policies and access
control mechanisms fall short on protecting the
privacy of the users. This paper presents a survey
on different frameworks for privacy enhanced
social networking websites, which technically
enforces the protection of the personal
information of a user, when interacting with social
applications.
Keywords: Privacy, Social Networking Sites, trust,
privacy watch, privacy crypt, Hippocratic Social
Network
1. Introduction
Social networking websites have become a potential target
for attackers due to the availability of sensitive
information as well as its large user base. Therefore,
privacy and security issues in online social networks are
increasing. Privacy issue is one of the main concerns, since
many social network users are not careful about what they
expose on their social network space. Many social
networking sites try to prevent the exploitations, but many
attackers are still able to overcome those security counter
measures by using different techniques. Social network
users may not be aware of such threats. A survey on
different privacy frameworks in social networking
websites to prevent from the privacy issues are discussed
in this paper.
2. OAUTH (Open Authorization) Framework
Mohamed Shehab et al. proposed OAuth framework. It
provides a secure and efficient mechanism for authorizing
third-party applications without releasing a user’s access
credentials. OAuth framework increases user privacy by
separating the role of users from that of third party
applications. Users need not share their private credentials
with third party application, instead OAuth issues a new
set of credentials. These new credentials are represented
via an Access Token. An access token is a string which
denotes a unique set of permissions granted to a third
party application. After getting the approval from the
resource owner, an authorization server issues access
token to the third party application. Authorization code
flow is shown in the figure 1. The authorization flow
process consists of three parties: End-user (resource
owner) at browser, Client (third-party application),
Authorization server (e.g., Facebook). When a third party
application needs to access user’s resources, it presents its
Access Token to the service provider. The authorization
server authenticates the end user, and decides whether to
grant or deny the third-party application’s access request
[1].
Figure 1. Authorization code OAuth flow.

3. Privacy Crypt
Robert Koch et al. implemented a prototype called Privacy
crypt. It uses two core modules (the crypto-module and
encoding module) and the page module. It is an extension
of well known Firefox browser. It ensures the privacy of
data released on public severs in the internet. When the
user requests a website, a small key-icon is shown in the
status bar of the Firefox browser. On clicking the icon, a
dialog box will be displayed. Figure 2 shows the dialog box
which asks the user to enter the key. The key is stored in
the database as strings. Once the key is entered, the user
can work just as usual. When the user sends a new status
message, privacy crypt encrypts it and sends the cipher to
the server. The user can not see the encrypted text. Both
the encryption and decryption process are fully
transparent and carried out in the background. The
friends of the user must enter this key. Then the cipher
text is decrypted and the readable message is shown as
usual. Therefore the user and his friends who know the
key can only read the message. Using Privacy crypt, the
user can control the distribution of her data in an easy and
effective way [2].
Figure 2. Dialog for entering a new encryption key
4. Privacy Management Models
Gorrell P Check et al. proposed two privacy management
models namely Group-based policy Management with
assisted friend grouping and Same-as Policy Management.
Gorrell P Check et al. predefined ten relationship groups:
family, close friends, graduate school, under graduate
school, high school, work, acquaintances, friends of friend,
community, and other. Group based policy management
helps the user to populate groups based on relationship. It
also assigns object permissions to the groups. Assisted
friend grouping assists in grouping their friends and
setting friend level exceptions within the group policy.
Figure 3 shows role based access control. Same-as policy
management leverages users’ memory and opinion of their
friends to set policies for other similar friends [3].
Figure 3. Role based access control.
5. Digital Image Authentication Model
The images from social networking websites can be easily
downloaded, edited, morphed and shared. The validation
of the ownership is practically impossible due to high
degree of replication. Nidhi Grover et al. proposed digital
image authentication model to secure the unintentional
use of personal images by someone else. Embedding
algorithm and extraction algorithm are used in this model.
The subscriber’s login detail and current timestamp will be
converted to binary and it will be hidden the original
image. It is done using embedding algorithm. Embedding
algorithm is shown in figure 4.
Figure 4. Embedding Block diagram

Figure 5. Extraction Block Diagram
If any fake user tries to use the original image for creating
a fake ID, SN provider compares it with the original
embedded data and detects the fake use. It is done by
extraction algorithm. Misuse of the images can be
prevented using the model is shown in figure 5 [4].
6. Multiparty Access Control (MPAC) model
Hongxin Hu et al. introduced MPAC model. MPAC model
enables the protection of shared data associated with
multiple users in SNS. MPAC model enforces privacy
concerns over data associated with multiple users in SNS.
It describes relationships of five individuals, Alice (A), Bob
(B), Carol (C), Dave (D), and Edward (E), along with their
groups of interests. Figure 6 shows an example of
Multiparty social networking. It shows that the users may
be directly connected by more than one edge labeled with
different relationship types. In addition, the users may
also have transitive relationship, such as friends-of-
friends (FOF), colleagues-of-colleagues (COC) and
classmates-of-classmates (LOL). It is noticed that some
data items have multiple controllers and some users may
be the controllers of multiple data items. It also shows that
the users can participate in the fashion and hiring group.
Some users can also join in multiple groups. Thus MPAC
model can greatly enhance the flexibility for regulating
data sharing in SNSs [5].
Figure 6. An example of multiparty social network
presentation.
7. Footlights
Jonathan Anderson et al. designed and implemented an
architecture called footlights. Footlight’s design constraint
is trust. Users need not trust any third party with their
private data.
Footlight uses cloud storage providers to hold user data.
This data is broken into fixed sized blocks and then
encrypted. When the users share with their friends,
footlights reveals encryption keys to the client software of

the chosen users only. The local Footlights client software
interprets blocks that have been shared with it as a file
system. The subsets of these data can be exposed to
applications through a security API. Figure 7 shows file
system layers visible to applications. Footlights provide an
API that lets applications work with user data but not leak
it beyond the user’s consent.
Applications bundle files in a directory to be shared with
other users, but footlights allows sharing only with explicit
expression of user intent. Applications can access data
such as photos directly if the user explicitly expresses
intent [6].
Figure 7. File system layers visible to applications.
8. Privacy watch
Esma Aïmeur et al. introduced privacy watch framework.
Privacy watch provides users with an easy and flexible to
specify and communicate their privacy concerns to other
users, third parties and SNS service provider. There are
four privacy levels: No Privacy, Soft Privacy, Hard Privacy
and Full Privacy. Based on his privacy level, the user can
determine how
Figure 8. Overview of Privacy Watch
much information he would like to share with the
provider. Figure 8 shows the overview of Privacy watch.
Server access control in privacy watch manages the access
control mechanism to the personal information of the user
for his friends. It acts as a proxy sitting between the
personal information of the user and his friends. The SNS
server is responsible for strong the personal information
of the user. The users have Client Privacy Manager. It is
responsible for helping the user to maintain their
sovereignty of their personal information. Privacy watch
relies on third party email server such as Hotmail and
Gmail. It is used to store and exchange encryption and
decryption keys as well as UPP (User Privacy Policy)
between users.
The user generates a public verification key for each group
and transmits it to the SNS server. Each friend of the
group will receive the private signature key through an
email. Using the private signature key, a friend of the user
can sign messages. The SNS server can use the public
verification key of the group to check the validity of the
signature. Thus Privacy watch fulfills the most of the
privacy criteria for a privacy-enhanced social networking
site [7].
9. Hippocratic Social Network
Rajneeshkaur Bedi et al. proposed Hippocratic Social
network to enhance the user profile privacy in online
social network. It has four components:
 Watermarking Module
 SNS Interaction Module
 Data Perturbation Module
 HSN Repository
Watermarking module inserts a visible or invisible
watermark in the database record of user profile. This
helps in tackling with the profile cloning attacks. SNS
interaction modules read the request for profile data. It
identifies the level of security that has to be added to the
profile data. Then it modifies and returns the profile data
to the requesting application. Data perturbation module
takes the record form SNS interaction module and
modifies it using generalization or suppression method.
HSN repository has Retention Table, Compliance Table,
Consent Table, Sharing Limit, Data Generalization and
suppression metadata. Retention table stores the time for
which the information will be disclosed to a third party
application or another person. Compliance table shows
that whether his profile data is disclosed to other user or
not. Consent table stores the user’s consent about his
profile data disclosure in regards of a particular group or a
person or a third party application. Sharing limit specifies
the number of time the information will be disclosed to a
particular user [8].
10. Trust model for SNS
Nafaa Jabeur et al. proposed a trust model for SNS. Trust
model deals with five trust aspects: What to trust? , When
to trust, Where to trust, whom to trust? and Why to trust?
Direct and indirect are two types of trust. Trust may have

risks and benefits. Context affects the trust. Trust
characterizes the relations of the SNS user with other SNS
users. The trust model helps in preventing the leakage of
personal data. It also helps in personalizing privacy and
trust to better fit the current context, user profile and data
context [9].
11. Recommendation for SNS users
Michael fire et al. presented eight recommendations for
SNS users to help them improve their security and privacy
in Social Network Websites [10]. The eight
recommendations are as follows:
 Remove Unnecessary Personal Information.
 Adjust Privacy and Security Settings.
 Do Not Accept Friend Requests From Strangers
 Install Internet Security Software
 Remove Installed Third-Party Applications
 Do Not Publish Your Location.
 Do Not Trust Your SNS Friends.
 Monitor Your Children’s SNS Activity.
12. Comparison of various Privacy Models
Foot light Model and OAuth Model authorize the third
party application to work with user data but not beyond
the user’s consent. Privacy Management Model helps to
populate groups based on relationship. Digital Image
Authentication Model is an efficient model to secure the
unintentional use of personal images by someone else.
Multiparty Access Control Model enforces privacy
concerns over data associated with multiple users in SNS.
Privacy Crypt Model, Privacy Watch Model, Hippocratic
Model and Trust Model help in protecting the privacy of
user profile data.
13. Conclusion
This paper presented the various privacy risks that a user
incurs while putting personal information on the Social
Networking Websites. It also exposed the privacy
frameworks for a privacy enhanced Social networking
websites, Social networking sites try to implement
different security mechanisms to prevent from the privacy
issues, and to protect their users, but attackers will always
find new methods to break through those defenses.
Therefore, social network users should be aware of all
these threats, and be more careful when using them.
Social network users should be careful about what they
expose on their social network space.
REFERENCES:
1. Mohamed Shehab et al., Recommendation Models for
Open Authorization, IEEE Transactions on dependable and
secure computing, Vol. 9, No. 4, July/August 2012.
2. Robert Koch et al., Data Control in Social Networks,
978-1-4577-0460-4/11/$26.00 ©2011 IEEE.
3. Gorrell P. Cheek et al., Human Effects of Enhanced
Privacy Management Models, IEEE Transactions on
dependable and secure computing, Vol. 11, No. 2,
March/April 2014.
4. Nidhi Grover et al., Digital Image Authentication Model
Based on Edge Adaptive Steganography, 2013 Second
International Conference on Advanced Computing,
Networking and Security.
5. Hongxin Hu et al., Multiparty Access Control for Online
Social Networks: Model and Mechanisms, IEEE
Transactions on Knowledge and Data Engineering, Vol. 25,
No. 7, July 2013.
6. Jonathan Anderson, Frank, Must Social Networking
Conflict with Privacy, Co published by the IEEE Computer
and Reliability Societies May/June 2013.
7. Esma Aïmeur, Sébastien Gambs, Ai Ho, Towards a
Privacy-enhanced Social Networking Site, 2010
International, Reliability Conference on Availability and
Security.
8. RajneeshKaur Bedi, Nitinkumar Rajendra Gove, V.M.
Wadhai ,Hippocratic Social Network ,2013 Fifth
International Conference on Computational Aspects of
Social Networks (CASoN).
9. Nafaâ Jabeur, Sherali Zeadally, Sergey Maydebura,
Improving Trust and Privacy Models in Social Networks,
978-1-4673-0229-6/12/$31.00 ©2012 IEEE.
10. Michael Fire et al., Online Social Networks: Threats and
Solutions, IEEE Communication surveys & Tutorials, Vol.
16, No. 4, Fourth Quarter 2014.

Author Name
Name of the
Privacy
Model
Features
Mohamed Shehab OAuth Model It uses the concept of Access Tokens to authorize the third
party applications
Robert Koch
Privacy Crypt
Model
Using Crypto module, Encoding module and Page module, it
can control the distributions of user's data in an easy and
effective way.
Gorrell P. Cheek
Privacy
Management
Models
It uses Clustering techniques to group the friends. We can
also set the policies for friends of friends.
Nidhi Grover
Digital Image
Authentication
Model
Using Embedding algorithm and Extraction algorithm, it
secures the unintentional use of personal images by someone
else.
Hongxin Hu
Multiparty
Access Control
Model
It enables the protection of shared data associated with
multiple users in SNS.
Jonathan Anderson and Frank
Footlights
Model
It provides an API that lets applications work with the user
data but not leak it beyond the user's consent.
Esma Aïmeur, Sébastien Gambs, Ai Ho
Privacy Watch
Model
Based on the privacy level set by the user, it protects the user
privacy.
RajneeshKaur Bedi ,Nitinkumar
Rajendra Gove, V.M. Wadhai
Hippocratic
Model
It protects the privacy of user profile data based on the user
consents. It uses different modules and tables.
Nafaâ Jabeur, Sherali Zeadally, Sergey
Maydebura
Trust Model
It helps in personalizing privacy and trust to better fit the
current text, user profile and data context.

A Survey on Privacy in Social Networking Websites

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