Predicting Privacy Policy Automatically To the User Uploaded Images on Content Sharing Sites
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The document discusses the proposal of an Adaptive Privacy Policy Prediction (A3P) system designed to automatically set privacy policies for user-uploaded images on social networking sites, addressing the challenges of maintaining privacy amidst the widespread sharing of sensitive information. The A3P system utilizes image metadata for classification and privacy policy prediction, aiming to simplify the privacy settings process for users. This approach seeks to mitigate the risks associated with personal information exposure due to the persistent nature of social media.
![38 International Journal for Modern Trends in Science and Technology
Volume: 2 | Issue: 05 | May 2016 | ISSN: 2455-3778IJMTST
Predicting Privacy Policy Automatically To
the User Uploaded Images on Content Sharing
Sites
Rashmi T.M1
| V. Chetan Kumar2
1PG Scholar, Department of Computer Science and Engineering, P.E.S. College of Engineering, Mandya,
Karnataka, India.
2Assistant Professor, Department of Computer Science and Engineering, P.E.S. College of Engineering,
Mandya, Karnataka, India.
Nowadays sharing of images is increasing through social networking sites but maintaining privacy is a
major problem. While sharing images users knowingly or unknowingly share their personal information. Due
to these incidents, there is a need of tool for setting privacy for their images. To address this need we propose
an adaptive privacy policy prediction (A3P) to set privacy for their images. We are considering the metadata
for predicting the privacy. Our Solution depends on image classification for image categories and predicting
privacy.
KEYWORDS: Online information services, web-based services.
Copyright © 2015 International Journal for Modern Trends in Science and Technology
All rights reserved.
I. INTRODUCTION
Images are the main key enablers for user to
connect in social networking sites. Sharing of
images takes place not only for known person or
group but also for social circles e.g., Google+, Face
book etc. However images may expose sensitive
information of user and they may lead to
unwanted risks and personal information
violations[2],[6].Due to persistent nature of social
media , it allows others to collect information of
owner of published content.[2],[5],[6]. This may
lead to expose of one’s personal information.
Therefore, in this paper we are implementing
adaptive privacy policy prediction(A3P) system
which provides risk free privacy settings
automatically by generating policies[1].
Existing System
Most social networking sites allow user to enter
their privacy preferences but users struggle to
maintain these types of policies.
Because user share lot of information on social
media putting privacy policies for each and
everything is difficult task and error-prone.
Disadvantage of Existing System
Images may expose sensitive information of user
and they may lead to unwanted risks and personal
information violations
Due to persistent nature of social media, it allows
others to collect information of owner of published
content.
This may lead to expose of one’s personal
information.
Proposed System
We propose an Adaptive Privacy Policy Prediction
(A3P) system, which aims to provide users a hassle
free privacy settings experience by automatically
generating personalized policies.
Advantages of Proposed System
The A3P-core focuses on analyzing each individual
user’s own image’s metadata.
User can set policies automatically by generated
policies.
II. LITERATURE SURVEY
Some studies are carried out to set policies
automatically. Bonneau et al.[3] proposed the
concept of privacy suites which recommend to users
a suite of privacy settings that “expert” users or
ABSTRACT](https://image.slidesharecdn.com/45-160517110707/85/Predicting-Privacy-Policy-Automatically-To-the-User-Uploaded-Images-on-Content-Sharing-Sites-1-320.jpg)
![39 International Journal for Modern Trends in Science and Technology
Predicting Privacy Policy Automatically To the User Uploaded Images on Content Sharing Sites
other trusted friends have already set, so that normal
users can either directly choose a setting or only
need to do minor modifications. Similarly,
Ravichandran et al.[7] studied how to predict a user’s
privacy preferences for location-based data based on
location and time of day. More recently, Klemperer et
al.[5] studied whether the keywords and captions
with which users tag their photos can be used to help
users more intuitively create and maintain
access-control policies.
III. SYSTEM ARCHITECTURE
3.1 A3P Framework
Privacy policies are privacy preferences
expressed by the user to protect their information
from unwanted disclosure. We define the privacy
policies as follows:
Definition: A privacy policy p of user u consists of the
following components:
Subject (S):A set of users socially connected
to user U
Data(D):A set of data items shared by u
Action(A):A set of action granted by u to S on D
Condition(C):A Boolean expression which must be
satisfied in order to perform the granted actions.
3.2 A3P Architecture
A3P stands for adaptive privacy policy prediction
system which helps users to derive the privacy
settings for their images. The A3P architecture
consists of the following blocks as shown in the
figure1:
1. Metadata based image classification
2. Adaptive policy prediction
3. Look-up privacy policies
4. Database.
A3P core classifies the images based on the
metadata and predict the policies depending on the
metadata. The look-up privacy policy looks if the
image or similar type of image already exists which
can be given with the similar privacy policies. If
similar type of image doesn’t exist then it looks for all
the policies and lets user choose the policies.
Fig 1: System architecture
3.3 A3P CORE
There are two major components in A3PCore:
1. Image classification
2. Adaptive policy prediction
3.3.1 Metadata-based image classification:
The Metadata-based image classification groups
images into sub-categories. The process consists of
following 3 steps:
Step 1: Extract keywords from the metadata
associated with an image. The metadata
considered are tags, caption and comments.
Identify all the nouns, verbs and adjectives in the
metadata and store them as metadata vectors.
where i, j and k are the total number of nouns,
verbs and adjectives respectively.
Step 2: Derive a representative hypernym ( h) from
each metadata vector. Retrieve the hypernym for
each in a metadata vector based on the Word net
classification and obtain a list of hypernym
where v: hypernym and f :frequency.
Step 3: Find a subcategory that an image belongs
to. At the beginning, the first image forms a
subcategory as itself and the representative
hypernyms of the image becomes the subcategory’s
representative hypernyms. We compute the
distance between representative hypernyms of a
new incoming image and each existing subcategory
[4].
3.3.2 Adaptive policy prediction
There are two major components in the Adaptive
policy prediction process.
1. Policy mining
2. Policy prediction
Policy mining is a process of mining policies for
similar categorized images and policy prediction
process for predicting the policy for user uploaded
images.
Policy mining: Privacy policies are privacy
preferences expressed by the user to protect their
information from unwanted disclosure. Policy
mining process deals with mining of policies by
applying various association rules and steps. This
hierarchical approach considers subject, action
and conditions. It includes following steps:
Step1: apply association rule mining on subject
component of the policies of the new image. Then](https://image.slidesharecdn.com/45-160517110707/85/Predicting-Privacy-Policy-Automatically-To-the-User-Uploaded-Images-on-Content-Sharing-Sites-2-320.jpg)
![40 International Journal for Modern Trends in Science and Technology
Volume: 2 | Issue: 05 | May 2016 | ISSN: 2455-3778IJMTST
select the best rule by considering the two
measures like support and confidence.
Step 2 : similar to the first step apply association
rule mining on action component . Then select the
best rule.
Step 3: mine the condition component in each
policy set. The best rules are selected which gives
us a set of attributes which often appear in policies.
Policy prediction: the policy mining process may
give us many number of policies, but our system
needs to show the best one to the user thus, this
approach is to choose the best policy for the user
by obtaining the strictness level. The strictness
level describes how strict a policy is. The strictness
can be discovered by two metrics: major level and
coverage rate. The major level is determined with
the help of combinations of subject and action in
policy and coverage rate is determined using the
condition component statement.
IV. CONCLUSION
We have proposed an Adaptive privacy policy
prediction (A3P) system to automate the privacy
policy settings for user uploaded images. And it
provides hassle free policies for user information
on social networking site.
REFERENCES
[1] Anna cinziz squicciarini, member,IEEE, Dan
Lin,Smith Sundareswaran and Joshua
Wede,“Privacy policy inference of user-uploaded
images on content sharing sites”.IEEE
TRANSACTIONS ON KNOWLEDGE AND DATA
ENGINEERING,VOL 27,NO.1, JANUARY 2015
[2] S. Ahern, D. Eckles, N. S. Good, S. King, M. Naaman,
and R. Nair, “Over-exposed?: Privacy patterns and
considerations in online and mobile photo sharing,”
in Proc. Conf. Human Factors Comput. Syst., 2007,
pp. 357–366.
[3] J. Bonneau, J. Anderson, and L. Church, “Privacy
suites: Shared privacy for social networks,” in Proc.
Symp. Usable Privacy Security, 2009.
[4] L. Geng and H. J. Hamilton, “Interestingness
measures for data mining: A survey,” ACM Comput.
Surv., vol. 38, no. 3, p. 9, 2006.
[5] P. Klemperer, Y. Liang, M. Mazurek, M. Sleeper, B.
Ur, L. Bauer, L. F. Cranor, N. Gupta, and M. Reiter,
“Tag, you can see it!: Using tags for access control in
photo sharing,” in Proc. ACM Annu. Conf. Human
Factors Comput. Syst., 2012, pp. 377–386.
[6] Y. Liu, K. P. Gummadi, B. Krishnamurthy, and
A. Mislove, “Analyzing facebook privacy settings:
User expectations vs. reality,” in Proc.
ACMSIGCOMMConf. Internet Meas. Conf., 2011, pp.
61–70.
[7] R. Ravichandran, M. Benisch, P. Kelley, and N.
Sadeh, “Capturing social networking privacy
preferences,” in Proc. Symp. Usable Privacy Security,
2009.
[8] R. A. Wagner and M. J. Fischer, “The string-to-string
correction problem,” J. ACM, vol. 21, no. 1, pp.
168–173, 1974.](https://image.slidesharecdn.com/45-160517110707/85/Predicting-Privacy-Policy-Automatically-To-the-User-Uploaded-Images-on-Content-Sharing-Sites-3-320.jpg)
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Predicting Privacy Policy Automatically To the User Uploaded Images on Content Sharing Sites
- 1. 38 International Journal for Modern Trends in Science and Technology Volume: 2 | Issue: 05 | May 2016 | ISSN: 2455-3778IJMTST Predicting Privacy Policy Automatically To the User Uploaded Images on Content Sharing Sites Rashmi T.M1 | V. Chetan Kumar2 1PG Scholar, Department of Computer Science and Engineering, P.E.S. College of Engineering, Mandya, Karnataka, India. 2Assistant Professor, Department of Computer Science and Engineering, P.E.S. College of Engineering, Mandya, Karnataka, India. Nowadays sharing of images is increasing through social networking sites but maintaining privacy is a major problem. While sharing images users knowingly or unknowingly share their personal information. Due to these incidents, there is a need of tool for setting privacy for their images. To address this need we propose an adaptive privacy policy prediction (A3P) to set privacy for their images. We are considering the metadata for predicting the privacy. Our Solution depends on image classification for image categories and predicting privacy. KEYWORDS: Online information services, web-based services. Copyright © 2015 International Journal for Modern Trends in Science and Technology All rights reserved. I. INTRODUCTION Images are the main key enablers for user to connect in social networking sites. Sharing of images takes place not only for known person or group but also for social circles e.g., Google+, Face book etc. However images may expose sensitive information of user and they may lead to unwanted risks and personal information violations[2],[6].Due to persistent nature of social media , it allows others to collect information of owner of published content.[2],[5],[6]. This may lead to expose of one’s personal information. Therefore, in this paper we are implementing adaptive privacy policy prediction(A3P) system which provides risk free privacy settings automatically by generating policies[1]. Existing System Most social networking sites allow user to enter their privacy preferences but users struggle to maintain these types of policies. Because user share lot of information on social media putting privacy policies for each and everything is difficult task and error-prone. Disadvantage of Existing System Images may expose sensitive information of user and they may lead to unwanted risks and personal information violations Due to persistent nature of social media, it allows others to collect information of owner of published content. This may lead to expose of one’s personal information. Proposed System We propose an Adaptive Privacy Policy Prediction (A3P) system, which aims to provide users a hassle free privacy settings experience by automatically generating personalized policies. Advantages of Proposed System The A3P-core focuses on analyzing each individual user’s own image’s metadata. User can set policies automatically by generated policies. II. LITERATURE SURVEY Some studies are carried out to set policies automatically. Bonneau et al.[3] proposed the concept of privacy suites which recommend to users a suite of privacy settings that “expert” users or ABSTRACT
- 2. 39 International Journal for Modern Trends in Science and Technology Predicting Privacy Policy Automatically To the User Uploaded Images on Content Sharing Sites other trusted friends have already set, so that normal users can either directly choose a setting or only need to do minor modifications. Similarly, Ravichandran et al.[7] studied how to predict a user’s privacy preferences for location-based data based on location and time of day. More recently, Klemperer et al.[5] studied whether the keywords and captions with which users tag their photos can be used to help users more intuitively create and maintain access-control policies. III. SYSTEM ARCHITECTURE 3.1 A3P Framework Privacy policies are privacy preferences expressed by the user to protect their information from unwanted disclosure. We define the privacy policies as follows: Definition: A privacy policy p of user u consists of the following components: Subject (S):A set of users socially connected to user U Data(D):A set of data items shared by u Action(A):A set of action granted by u to S on D Condition(C):A Boolean expression which must be satisfied in order to perform the granted actions. 3.2 A3P Architecture A3P stands for adaptive privacy policy prediction system which helps users to derive the privacy settings for their images. The A3P architecture consists of the following blocks as shown in the figure1: 1. Metadata based image classification 2. Adaptive policy prediction 3. Look-up privacy policies 4. Database. A3P core classifies the images based on the metadata and predict the policies depending on the metadata. The look-up privacy policy looks if the image or similar type of image already exists which can be given with the similar privacy policies. If similar type of image doesn’t exist then it looks for all the policies and lets user choose the policies. Fig 1: System architecture 3.3 A3P CORE There are two major components in A3PCore: 1. Image classification 2. Adaptive policy prediction 3.3.1 Metadata-based image classification: The Metadata-based image classification groups images into sub-categories. The process consists of following 3 steps: Step 1: Extract keywords from the metadata associated with an image. The metadata considered are tags, caption and comments. Identify all the nouns, verbs and adjectives in the metadata and store them as metadata vectors. where i, j and k are the total number of nouns, verbs and adjectives respectively. Step 2: Derive a representative hypernym ( h) from each metadata vector. Retrieve the hypernym for each in a metadata vector based on the Word net classification and obtain a list of hypernym where v: hypernym and f :frequency. Step 3: Find a subcategory that an image belongs to. At the beginning, the first image forms a subcategory as itself and the representative hypernyms of the image becomes the subcategory’s representative hypernyms. We compute the distance between representative hypernyms of a new incoming image and each existing subcategory [4]. 3.3.2 Adaptive policy prediction There are two major components in the Adaptive policy prediction process. 1. Policy mining 2. Policy prediction Policy mining is a process of mining policies for similar categorized images and policy prediction process for predicting the policy for user uploaded images. Policy mining: Privacy policies are privacy preferences expressed by the user to protect their information from unwanted disclosure. Policy mining process deals with mining of policies by applying various association rules and steps. This hierarchical approach considers subject, action and conditions. It includes following steps: Step1: apply association rule mining on subject component of the policies of the new image. Then
- 3. 40 International Journal for Modern Trends in Science and Technology Volume: 2 | Issue: 05 | May 2016 | ISSN: 2455-3778IJMTST select the best rule by considering the two measures like support and confidence. Step 2 : similar to the first step apply association rule mining on action component . Then select the best rule. Step 3: mine the condition component in each policy set. The best rules are selected which gives us a set of attributes which often appear in policies. Policy prediction: the policy mining process may give us many number of policies, but our system needs to show the best one to the user thus, this approach is to choose the best policy for the user by obtaining the strictness level. The strictness level describes how strict a policy is. The strictness can be discovered by two metrics: major level and coverage rate. The major level is determined with the help of combinations of subject and action in policy and coverage rate is determined using the condition component statement. IV. CONCLUSION We have proposed an Adaptive privacy policy prediction (A3P) system to automate the privacy policy settings for user uploaded images. And it provides hassle free policies for user information on social networking site. REFERENCES [1] Anna cinziz squicciarini, member,IEEE, Dan Lin,Smith Sundareswaran and Joshua Wede,“Privacy policy inference of user-uploaded images on content sharing sites”.IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,VOL 27,NO.1, JANUARY 2015 [2] S. Ahern, D. Eckles, N. S. Good, S. King, M. Naaman, and R. Nair, “Over-exposed?: Privacy patterns and considerations in online and mobile photo sharing,” in Proc. Conf. Human Factors Comput. Syst., 2007, pp. 357–366. [3] J. Bonneau, J. Anderson, and L. Church, “Privacy suites: Shared privacy for social networks,” in Proc. Symp. Usable Privacy Security, 2009. [4] L. Geng and H. J. Hamilton, “Interestingness measures for data mining: A survey,” ACM Comput. Surv., vol. 38, no. 3, p. 9, 2006. [5] P. Klemperer, Y. Liang, M. Mazurek, M. Sleeper, B. Ur, L. Bauer, L. F. Cranor, N. Gupta, and M. Reiter, “Tag, you can see it!: Using tags for access control in photo sharing,” in Proc. ACM Annu. Conf. Human Factors Comput. Syst., 2012, pp. 377–386. [6] Y. Liu, K. P. Gummadi, B. Krishnamurthy, and A. Mislove, “Analyzing facebook privacy settings: User expectations vs. reality,” in Proc. ACMSIGCOMMConf. Internet Meas. Conf., 2011, pp. 61–70. [7] R. Ravichandran, M. Benisch, P. Kelley, and N. Sadeh, “Capturing social networking privacy preferences,” in Proc. Symp. Usable Privacy Security, 2009. [8] R. A. Wagner and M. J. Fischer, “The string-to-string correction problem,” J. ACM, vol. 21, no. 1, pp. 168–173, 1974.