International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 04 Issue: 07 | July -2017
p-ISSN: 2395-0072
www.irjet.net
A Survey on Online Secure Social Networking with Friend Discovery System Priyanka S. Helode¹, Prof.K.H.Walse², Prof. M.U. Karande³ 1M.E.
Student, Dept. of Computer Engineering, Padm. Dr. V. B. Kolte College of Engineering and Technology, Malkapur (M.S.),. 2 Professor, Dept. of computer science and Engineering, Anuradha Engineering College, Chikhali (M.S.), 3 Assistant Professor, Dept. of computer science and Engineering, Padm. Dr. V B Kolte College of Engineering and Technology, Malkapur, --------------------------------------------------------------------------***----------------------------------------------------------------------------
Abstract - Now a days uses of social networking sites are
In the given paper, we have used the algorithm like AES, pattern matching, association rule mining, opinion mining and clustering. For security purpose, we used AES algorithm of 256 bit round key block.
increasing day by day due to social revolution in web. Normally in social networking sites users can easily registered, make a friends and communicate with people easily. In a social network, we can identify the identical users by using information of users which is stored in data base server. In this survey paper, we proposed the friend discovery system in which we recommend a friend to user accordingly to the lifestyle, behavior, ratings and profile analysis of users. In a existing system, it recommended a friend according to the location of users which is not suitable method because it is not necessary to match the users thinking or nature who live around us. In this paper, we also provide a security using AES Algorithm.
2. LITRATURE SURVEY Joonhee Kwon and Sungrim kim implemented a friend suggestion technique using the physical and social context. The system considers friendship from users, who shared same physical location. Using social context, the system recognized precise friendship such as social network. And after that, according to physical and social context the systems associate both the friendship. [1] Making a friendship is user related activity that comes in different forms. [1].Author uses the concept of the intangible friendship and the social friendship. The intangible friendship is the relation based on similar activity such as high overlap in tag usage. The social friendship is based on user related relation. They granted both the intangible friendship and the social friendship for enumerate friendship. Context has hardly been used for calculating friendship so far. In spite of this, it is used to take the current context into account. The context is arranged into physical context ,which uses current user location, time, also social context uses the social network of the user [1].
Keywords: Friend recommendation, social networks, life style, security, comments.
1. INTRODUCTION Social Networking is the discovery in the web history to share the online social life of users. Due to the social networking, many peoples are connected and exchange their feeling, thoughts with each other and improve the social relationship. Now a day, social network giving the platform for making friends, the suggestion provided in friend discovery system according to lifestyle of users, their interest, their thinking etc. In a given paper, we implemented friend discovery system which recommended a friend in a social networking on the basis of user’s profile a sit go through user’s profile information like name, city, education etc. On the basis of user’s lifestyle, it has predefined form format, with the help of this users just need to tick their daily activities, next it as rating with the help of rating users can like or dislike human data and analyzing the comments given by the users and categorize their comments in a positive or negative manner.
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L.Gou, F.You, J,Guo, L.Wu and X.L.Zhang proposed a unique system to support users to analyze and find out friends with various interests.[2] Author first abstract the information about user’s interest which is based on tags generated by users in a social network service context, then they build tag networks to match a user’s interest and a hierarchical tag system including a knowledge structure shared among those people who developed them. Then, measure similarities between user with the information of tag networks and social networks to suggest friends for users. they developed a visualization system, Social
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