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  • Subject area(s): Marketing
  • Price: Free download
  • Published on: 14th September 2019
  • File format: Text
  • Number of pages: 2

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data from the social networks a striking discovery was made: just by looking at a person's online friends, they could predict whether the person was gay or not. They did this with a software program that looked at the gender and sexuality of a person's friends, and using statistical analysis, made a prediction. Two students had no way of checking all of their predictions, but based on their own knowledge outside the internet world, their computer program appeared quite accurate for men, they said.”  In [6], researchers studied the information disclosure in social networks, and they found that by looking at certain characteristics, such as knowing which groups people belong to or their favorite applications, it was possible to predict their political affiliation. Facebook has recently announced new privacy settings. Although there are major improvements, researchers and experts continue to criticize  these settings. In [7], Canadian Privacy Commissioner  published a must-read report about personal information protection on Faceebook. This report clearly supports our idea of improving and simplifying the privacy, but it does not go beyond further than being a criticism. We believe that our study will inspire Facebook developers to implement more user friendly, more successful privacy management features.  All of the recent researches show the importance of protecting information in social networks. Lack of the privacy in social networks has become a big problem prompting some members to unregister so as to protect their privacy. Our study differs from recent studies. Instead of proving the existence of privacy problems and presenting attacks, we proposed a solution and its implementation for current problems that social network users encounter.  III. IMPLEMENTATION Our study provides an implementation of a web based solution for protecting privacy and information. It helps the users to automatically categorize a large number of friends into meaningful lists. The main assumption we make to build the social circle is that users would mostly present same information to all friends in a social group, and therefore social circles provide a meaningful categorization of friends for setting privacy and security policies [8, 9, 10]. For example, people in a company marketing team are friends of each other and personal information propagates easily among them. Hence, team members would probably want to present the same profile to all other members and thus set the same privacy policy for all of them. In this example, our application recognizes the marketing team as a social circle of user.  In our approach, we developed an Internet based application which finds social groups and circles for users in their social networks, and it provides the following features:  

A. Building Visual Graph of Social Groups Our system produces such a graph that helps users to see each social group and helps the user to make better decisions about his/her application and privacy settings. B. Privacy-Settings To Protect Personal Information Our approach suggests the set of friend lists that users should create, and the friend lists into which they should put each of their current friends based on the identified social groups. IV. CLUSTERING Methods for clustering have been deeply studied. But our aim is not to study them.  Clustering is just one of the steps to achieve our privacy goal. Our main aim is to use the right clustering algorithm for social networks and develop an approach to provide privacy by adapting this clustering algorithm to our application. The clustering in social networks requires grouping users into classes based on their attributes, properties of personal relationship, web page links, spreads of messages and other applications. It is the process of organizing users into groups whose members are similar in some way. Our algorithm is different from other clustering algorithms, and it can dynamically group users in a social network into different classes based on their properties and effectively identify relations among classes. It collects some data which are similar between social network users and are dissimilar to the users belonging to other groups. It creates active cells like network grids and builds 3D visual graph of social groups. The similar structure applied in the algorithm [10, 11] for finding (α, β) clusters has been used in our algorithm. In our application, friends sharing common personal information are the adjacent nodes to α-fraction. On the other hand those friends not sharing common personal information are the adjacent nodes to β-fraction. It is therefore possible to use the social graph of network users as an input to our algorithm. One might ask that what if a friend belongs to more than one group? For example, a user can have a friend from high-school or university that is currently his/her work mate. The overlapping sets or being in more than one group does not cause a problem from the privacy perspective. Our approach groups friends according the common information that a user wants to share with his/her friends. For example, if we just want to share our photos and status with our high-school friends, then we will be showing them a profile where they will only able to see our photos and status. If there are some other friends that we just want to share our photos and status, they will also be in this group. Therefore, it is perfectly normal and possible that a person can be in one or more social groups. The user will show some information in one group and will show different information in another group. In other words, we will limit who sees what. Fig. 1 and 2 show our clustering algorithm and a visual graph of clustering process for the users in a social network, respectively.  

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