- Open Access
- Total Downloads : 17
- Authors : R. Ramya, S. Supriya, S. Vinitha, A. P. V.Raghavendra
- Paper ID : IJERTCONV4IS19048
- Volume & Issue : NCICCT – 2016 (Volume 4 – Issue 19)
- Published (First Online): 24-04-2018
- ISSN (Online) : 2278-0181
- Publisher Name : IJERT
- License: This work is licensed under a Creative Commons Attribution 4.0 International License
Secure Authentic Image Injection Scheme in Social Networking
Ramya1, S. Supriya2, S. Vinitha3
1,2,3UG Scholar,8 Sem,
Department of CSE, V.S.B Engineering College, Karur, TamilNadu, India.
Department of CSE, V.S.B Engineering College, Karur, TamilNadu, India
At present social networking are best place for sharing information between friends and relatives these sites helps users to share daily in order, update daily news, share education information, update latest jobs postings etc. Users can get in touch with old friends and video chat with friends. Project design must be in Asp.net programming language and SQL server. Members should have secured user login and users data must be private. Users must have features to search friends, send friend request, add friends, post comments and scraps, uploaded images, block, delete existing users. Quick progress in the world has given a different form for communication over the computers for the past few years. Apart from email, this form of interactions allows different users to share their information to the desired people all around the world through a common medium. The common example for such a medium is Social Networking, which is a web application used for incorporating different kinds of communities for people who share a common attention or actions.
II. RELATED WORK
Our work is related to privacy setting configuration in social networks and privacy analysis of online content sharing in social network.
The Policy Prediction algorithm is used to share the contents and images for friends, family members and co- workers through the social network. For example Facebook. In our proposed system we select friends from the friend list,
that they only can view our shared contents and the uploaded images.
Content Based Filtering in online social network by exploiting ML techniques[2,3,6] The aim of the system is to experimentally appraise an automated system called Filtered Wall. It is able to filter unwanted messages from user wall
which is achieved by using Flexible rule based system. The idea of proposed system is filtering the unwanted post from
the home page in OSN wall.
A user can follow any other user, and the user being followed need not follow back. In face book the user can follow any others without their permission. Here we provide the follower request to follow others.
In facebook anyone can view our profile without our knowledge. In the proposed system if any violation is found, automatically it will send the notification to the concern account holder.
III PROPOSED SYSTEM
In our architecture user and admin plays a major role. Admin monitors N number of users and their policies, an also it monitors Online Social Network Wall and database of social network. Database is a collection of data. It is used to store large amount of data and retrieval of data which is shared in the social network. A3P has a collection of policy. These policies are created and controlled by the administrator.
News feed filtering
Our policies are inspired by popular content sharing sites (eg, Facebook), although the actual implementation depends on the specific content-management site structure and implementation. When a user uploads an image, the image will be first sent to the A3P. The A3P classifies the policy mining and policy prediction, determines whether there is a need to invoke the A3P-social. In most cases, the A3P predicts policies for the users directly based on their historical behaviour.
Hierarchical mining approach for policy mining is proposed. Our approach leverages association rule mining techniques to discover popular patterns in policies. Policy mining is carried out within the same category of the new image because images in the same category are more likely under the similar level of privacy protection. The basic idea of the hierarchical mining is to follow a natural order in which a user defines a policy. Given an image, a user usually first decides who can access the image, then thinks about what specific access rights should be given, and finally refine the access conditions such as setting the expiration date. Correspondingly, the hierarchical mining first look for popular subjects defined by the user, then look for popular actions in the policies containing the popular subjects, and finally for popular conditions in the policies containing both popular subjects and conditions.
The policy mining phase may generate several candidate policies while the goal of our system is to return the most promising one to the user. Thus, we present an approach to choose the best candidate policy that follows the users privacy tendency. It stores the users policy performs the action according to it.
User Id: Mobile number or Mail id. A: Selecting Friend
B: Follower Request C: News Feed Filtering D: Block Person
If the user accecpt the policy which are presented in the facebook settings, the administrator will send the YES decree to the database else if it is not found then the administrator will send the NO command to the database which are used for providing high security.
Selecting Friend: Used for selecting the friend from the friend list. Only these selected friends can view our shared contents and images. It will not be viewed by the unselected friends as it is filtered by the administrator.
Follower Request: User can send the follower request to anyone. Only if the request is accepted by that person, The user can follow him/her. Most content sharing websites allow users to enter their privacy preferences. Unfortunately, recent studies have shown that users struggle 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 tedious and error-prone. Therefore, many have acknowledged the need of policy recommendation systems which can assist users to easily and properly configure privacy settings.
Content based filtering
Content based filtering
NewsFeed Filtering: In this system admin filters unwanted content in OSN wall, which is achieved by key word filtering technique. In this technique admin has a collection of keys , if that key matches with the user OSN wall that particular contents or post filtered by admin knowledge.
User interface Admin Block Person:
In before block: A,B&C are friends to each other. If anyone from the three friends share the images or the contents it can be viewed by the other two friends.
In after block: If A blocks B, then the details of A will not be viewed by B, but A & C are friends, so the details of A can be viewed by C, but in our system through c also B cannot view the details of A.
A blocked B
connection C share A post (Blocked)
Notification: Our proposed system has a notification feature, which is controlled by admin. If the violation occurs in any policy the notification send or indicate to the concern account holder.
The A3P-core focuses on analyzing each individual users own images and metadata, while the A3P-Social offers a community perspective of privacy setting recommendations for a users potential privacy improvement. The rule layer adopted for filtering unwanted posts has been introduced. When a user tries to access the image without permission, the owner receives an alerting message if it is blocked by OSN wall. If the violation is found, the notification will send to the user.
also send notification, E-Mail to that who has posted unwanted message on wall. This might enhance services provided by OSN. Black List and Filtering Rule specification are made easier by development of GUI and a set of related tools. Along with it, our proposed system provides a better accuracy for classification of message as compare to previous implemented methods.
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Ms. R.RAMYA pursuing her BE CSE in V.S.B. Engineering College under Anna University. She had participated in several workshop and symposium and presented papers.
MS. S.SUPRIYA pursuing her BE CSE in V.S.B. Engineering College under Anna University. She had participated in several workshop and symposium and presented papers.
MS. S.VINITHA pursuing her BE CSE in V.S.B. Engineering College under Anna University. She had participated in several workshop and symposium and presented papers.
Mr.A.P.V.Raghavendra is pursuing Ph.D. from the Manonmanium Sundaranar University, Tirunelveli, India from 2013 onwards and Completed M.Tech(CSE) degree from Bharath University, Chennai, India in 2009. He is a Member in ISTE New Delhi, India, IAENG, Hong Kong. Now He is currently working as an Assistant Professor in Computer Science and Engineering in V.S.B Engineering College, Karur, Tamil Nadu, and India. His research interests include Data Mining, Data Bases, Artificial Intelligence,.,He had published 2 Annexture II Scopus indexed and 18 international publications.