A Survey On Privacy Policy Inference for Social Images

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395 -0056
Volume: 03 Issue: 02 | Feb-2016 www.irjet.net p-ISSN: 2395-0072
© 2016, IRJET | Impact Factor value: 4.45 | ISO 9001:2008 Certified Journal | Page 886
A Survey On Privacy Policy Inference for Social Images
Miss. Chaitrali A. Salunke
Student, Computer Engineering Department, V.P College of Engineering, Baramati, Maharashtra, India
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Abstract - Social media has become one of the
ultimate important parts of our daily life as it enables
us to communicate with a many people. Creation of
social networking sites such as Facebook LinkedIn etc,
individuals are given opportunities to meet new friends
in their own and also in the other variety of
communities across the world. So that many of the
photo sharing applications or content sharing
applications on these sites allow users to annotate
photos with those who are in them. A number of
researchers have studied the social uses and privacy
issues of online photo sharing or content sharing sites ,
but less have explored the privacy issues of photo
sharing in social networks. Users of social-networking
services share abundant information with numerous
“friends.” This improved technology causes to privacy
violation where the users are sharing the enormous
volumes of images across more number of peoples. This
privacy need to be taken care in order to make better
the user satisfaction level. Towards or focusing on this
need, using Adaptive Privacy Policy Prediction (A3P)
system to assist users compose privacy settings for
their images. Our main contribution to the existing
work is to generate user profile, further the privacy
inference policies should be mainatained with respect
to user profile.
Key Words: Social media; content sharing.
1. INTRODUCTION
Creating privacy controls for social media or networks
that are both expressive and usable is a major challenge.
The word “social media” refers to the outspread of
Internet-based and mobile services that allow users to
participate in online exchanges, bring user-created
content, or join online communities. Online social
networks or sharing/content sites are websites that allow
users to build or construct connections and relationships
to other Internet users. Social networks store information
remotely, rather than on a user’s personal computer.
Social networking can be used to keep in touch with
friends, make new friends and find people with similar
interests and ideas. The relation between privacy and a
person’s social network is multi-faceted. So it required to
develop more security mechanisms for different
communication technologies, especially online social
networks. Privacy is very important to the design of
security mechanisms. Most social networks providers
have provide an opportunity of privacy settings to allow or
refuse others access to personal information details. In
certain event or an occasion we want information about
ourselves to be known only by a small circle of close
friends, and not by strangers or unknown people. In other
side, we are willing to reveal our personal information to
strangers, but not to those who know us better. Social
network theorists have studied the relevance of relations
of different depth and strength in a person’s social
network and the valued of so-called weak ties in the flow
of information across different nodes in a network. An
Internet privacy can be define as the ability to control
what information one reveals about oneself, and who can
access that information. Essentially, when the data is
gathered or analyzed without the knowledge or
permission of its owner, privacy is violated. When it comes
to the usage of the data, the owner should be informed
about the purposes and aim for which the data is being or
will be used. Most content sharing or photo sharing
websites permit users to enter their privacy preferences.
Unfortunately, recent studies have shown that users
struggle or it is difficult to set up and maintain such
privacy settings. One of the main reasons provided is that
given the amount of shared information this process can
be tiresome and error-prone. Therefore, many have
recognized the need of policy recommendation systems
which can help users to easily and properly configure
privacy settings. However, existing system for automating
privacy settings appear to be insufficient to address the
unique privacy needs of images due to the amount of
information absolutely carried within images and their
relationship with the online environment wherein they
are exposed. The privacy of user data can be given by
using two methods.
1. The user can enter the privacy preferences
2. Usage of recommendation systems which helps users
for setting the privacy preferences.

The privacy policy of user uploaded data can be provided
depends on the user social environment and personal
characteristics. Social context of users, such as their profile
information and relationships with others may provide
beneficial information regarding users’ privacy
preferences. The privacy policy for image which is
uploaded by user can be provided depend on the user
uploaded image’s content and its metadata. A hierarchical
image classification which classifies images first based on
their contents and then decides each category into
subcategories based on their metadata. Images that do not
have metadata will be classed together only by content.
Such a hierarchical classification provides by A3P system
which gives a higher priority to image content and reduces
the influence of missing tags[1].
2. LITERATURE SURVEY
Many studies and analysis have been performed on
privacy policy techniques.
Alessandra Mazzia et al. [2012] introduced PViz
Comprehension Tool , an interface and system that
corresponds more directly with how users model groups
and privacy policies applied to their networks. It allows
the user to understand the Visibility of her profile
according to automatically natural sub-groupings of
people. Because the user must be able to identify and
differentiated automatically-constructed groups, we also
address the important sub-problem of producing effective
group labels. PViz tool is better than other current policy
comprehension tools Facebook's Audience View and
Custom Settings page.
Peter F. Klemperer et al. [2012] developed a tag based
access control of data shared in the social media sites. A
system that generates access-control policies from photo
management tags. Every photo is incorporated with an
access grid for mapping the photo with the user’s friends.
The participants can select a suitable preference and
access the information. Photo tags can be divided as
organizational or communicative based on the user needs.
There are several important limitations to our study
design. First, our results are limited by the participants we
recruited and the photos they provided. A second set of
disadvantages concerns our use of machine generated
access-control rules. The algorithm has no access to the
context and meaning of tags and no insight into the policy
the participant intended when tagging for access control.
As a result, some rules appeared strange to the
participants, potentially driving them toward explicit
policy-based tags like “private” and “public.
Sergej Zerr et al. [2012] proposed a technique Privacy-
Aware Image Classification and Search to automatically
detect private images, and to enable privacy-oriented
image search. It combines textual meta data images with
variety of visual features to give security policies. In this
the selected image features (edges, faces, color
histograms) which can help discriminate between natural
and man-made objects that can indicate the presence or
absence of particular objects (SIFT). It uses various
classification models trained on a large scale dataset with
privacy assignments obtained through a social annotation
game.
Choudhury et al. [2009] proposed a recommendation
framework to connect image content with communities in
online social media. They characterize images through
three types of features: visual features, user generated text
tags, and social interaction, from which they recommend
the most likely groups for a given image.
Similarly, an automated recommendation system for a
user’s images to provide suitable photo-sharing groups.
Jonathan Anderson et al. [2009] proposed Privacy Suites
which allows users to easily choose “suites" of privacy
settings. A privacy suite can be created by an expert using
privacy programming. The privacy suite is distributed
through distribution channels to the members of the social
sites. The drawback of a rich programming language is
less understandability for end users. Given a sufficiently
high-level language and good coding practice, motivated
users should be able to verify a Privacy Suite. The main
goal is transparency, which is essential for convincing
influential users that it is safe to use.
Ching-man Au Yeung et al. [2009] proposed a access
control system based on a decentralised authentication
protocol, descriptive tags and linked data of social
networks in the Semantic Web. It allows users to create
expressive policies for their photos stored in one or more
photo sharing sites, and users can specify access control
rules based on open linked data provided by other parties.
Danezis et al. [2009] proposed a machine-learning based
approach to automatically extract privacy settings from
the social context within which the data is produced. It
develop privacy settings based on a concept of “Social
Circles” which consist of clusters of friends. User’s privacy
preferences for location-based data based on location and
time of day.
Fabeah Adu-Oppong et al. [2008] developed concept of
social circles. It provides a web based solution to protect
personal information. The technique named Social Circles
Finder, which automatically generates the friend’s list. It is
a technique that analyses the social circle of a person and
identifies the intensity of relationship and therefore social
circles obtained a meaningful categorization of friends for
setting privacy policies. The application will identify the
social circles of the subject but not show them to the
subject. The subject will then be asked questions about

their willingness to share a piece of their personal
information. Based on the answers the application finds
the visual graph of users.
Kambiz Ghazinour et al. [2008] designed a recommender
system known as Your Privacy Protector that
understands the social net behavior of their privacy
settings and recommending privacy options. It uses user’s
personal profile, User’s interests and User’s privacy
settings on photo albums as parameters and on the basis
of these parameters the system constructs the personal
profile of the user. It automatically learned for a given
profile of users and assign the privacy options. It allows
users to see their current privacy settings on their social
network profile, namely Facebook, and detects the
possible privacy risks. Based on the risks it adopts the
necessary privacy settings.
Fang et al. [2007] proposed a privacy wizard to assist
users grant privileges to their friends. The wizard asks
users to first assign privacy labels to selected friends, and
then uses this as input to construct a classifier which
divides friends based on their profiles and automatically
assign privacy labels to the unlabeled friends.
3. CONCLUSIONS
This paper describes various privacy policy techniques for
user uploaded images in various content sharing sites. The
privacy policy can be applied based on the user social
behavior and the user uploaded image content. Privacy
policy techniques among the existing systems. Future
research leads towards improving the performance by a
novel semantic retrieval of images.
REFERENCES
1. Anna Cinzia Squicciarini, Member, IEEE, Dan Lin, Smitha
Sundareswaran, and Joshua Wede, “Privacy Policy
Inference of User- Uploaded Images on Content Sharing
Sites” IEEE Transaction On Knowledge And Data
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2. A. Mazzia, K. LeFevre, and A. E.,, “The PViz
comprehension tool for social network privacy settings,”
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Bauer, L. F. Cranor, N. Gupta, and M. Reiter, “Tag, you can
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in Proc. Symp. Usable Privacy Security, 2008.

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