Primary Hop Friend Recommendation System in Online Social Network

(1)

International Journal of Electrical, Electronics and Computer Systems (IJEECS)

________________________________________________________________________________________________

ISSN (Online): 2347-2820, Volume -4, Issue-8, 2016 47

Primary Hop Friend Recommendation System in Online Social Network

1Lakshmi H S, ²Vartika Sharma, ³Syed Thouheed Ahmed

1,2Dept. of CSE, GSSSIETW, Mysuru, Karnataka, India, Affiliated to Visvesvaraya Technological University

3Sr.Research Engineer, ThinkSoft Research and Information Technologies, Bengaluru, Karnataka, India

ABSTRACT: The OSN is most combined way of approaching and making a networking model and hence the system is aimed to achieve the overall segment of OSN.

OSN is about providing system feasibility over communicating and in this paper, the system behavior for analyzing and understanding friend recommendation and its privacy policy protocol system is simulated. The results are achieved under simulative analysis from Microsoft environment.

I. INTRODUCTION

An online social network is a platform to build social network or social relations among people who shares similar interests, activities, backgrounds or real-life connections. A social network service consists of a representation of each user, his or her social links, and a variety of additional services such as career services.

Social network sites are web-based services that allow individual to create a public profile, create a list of users with whom to share connections, and view and cross the connections within the system. Most social network services are web-based and provide means of users to interact over the internet.

Facebook and other social networking tools is increasingly the object of scholarly research. Scholars in many fields have begun to investigate the impact of social networking sites, investigating how such sites may play into issues of identity, privacy,social capital, youth culture, and education. Research has also suggested that individuals add offline friends on Facebook to maintain contact and often this blurs the lines between work and home lives. According to a study in 2015, 63% of the users of Facebook or Twitter in the USA consider these networks to be their main source of news, with entertainment news being the most seen. In the times of breaking news, Twitter users are more likely to stay invested in the story. In some cases when the news story is more political, users may be more likely to voice their opinion on a linked Facebook story with a comment or like, while Twitter users will just follow the sites feed and/ or re-tweet the article.

In all these aspects the communication between the users of the social networking sites is a basic need where

they try to exchange their information. So users use friend recommendation system to communicate with each other where there are many security breaches in the existing single hop system.Hence multi hop trust chain is established for friend recommendation by using the concept ofprioritizes for privacy and online privacy preserving algorithm is implemented.

II. MOTIVATION

The proposed system designs a simulative analysis and has increased the performance range of OSN and has expanded the network worldwide which is currently the fastest growing network. Therecommendation is made for interconnecting each module to another based on mutual simplifies and attributes and thus the same shall be discussed as this is considered as a challenge for simulative misuse of social information. In this paper a recommendation scheme is proposed and hence the same is discussed.

III. RELATED WORK

Web-based social networking services make it possible to connect people who share interests and activities across political, economic, and geographic borders.

Through e-mail and instant messaging, online communities are created where a gift economy and reciprocal altruism are encouraged through cooperation. Information is suited to a gift economy, as information is a non-rival good and can be gifted at practically no cost. Scholars have noted that the term "social" cannot account for technological features of the social network platforms alone. Hence, the level of network sociability should determine by the actual performances of its users.

From an intense survey, the system behavior is monitored and maintained to achieve a general approach for recommending friends and thus the privacy is decreased in order to maintain the same. Many techniques have been discussed based on OSN privacy Fong et al. [1] propose anaccess model that formalize and generalize the privacy preservation mechanism for Facebook. Carminati. et al. also propose an access control mechanism for the informationsharing in web-

(2)

________________________________________________________________________________________________

based social networks (a.k.a. online social networks) in [2], which jointly considers the relationship type, trust metric, and degree of separation in the policy design.

The major difference between their scheme and the work in [1] and ours is that they use the decentralized architecture for the access control, which may incur potential security breaches, like fabricating identity, attributes, and trust information. Along this line of research, Squicciarini et al. in [3], [4] also discuss about the comprehensive approaches used to providing privacy of social network users.

In terms of discovering friendships, Daly and Haahr in [5] discuss the establishment of friendship chains using social attributes. Similarly, Chen and Fong in [6] use trust factor in collaborative filtering (CF) algorithm to recommend OSN users on Facebook, where they analyze the similarity based on users’ interests and attributes. One of their following work has the same idea, but try to use data mining approach to gather users’

information to input to CF algorithm for recommendation, Dhekane and Vibber discuss thfriend finding problem on the Federated social networks.

However, the above works fail to consider users’Privacy concerns on both identity and their social attributes.

IV. SYSTEM METHODOLOGY

The proposed system is designed to receive a standard review on achieving and maintaining the system behavior under privacy policy as discussed in Fig 1.

Fig 1: System Architecture Diagram

The proposed system consists of a user profile under a registered domain of work and each module is been discussed, the users are interconnected and are active under this social network. Each node generated is under

a recommendation checksum as shown in Fig1. , the approving mechanism is presented under a valid node of verification.

The system architecture consists of users and admin based monitoring. The users are active under social networking and each node contributes in formation of the above discussed and shown network. The users are authenticated and hence prove to provide an authentication for login for false representation. The system behavior is analyzed under a technique of monitoring and analysis. The administrator is hyper active and has many privileges for monitoring and task scheduling under any annoying activities.

Fig 2: High-Level Design

In Fig 2, the system high level design is been discussed and this diagram provides high level analysis of network. The model consist of user and group of other users under connected via direct and in-direct connections, the system focus on friend recommendations and privacy policy preserving. Each node is constantly monitored and hence the system is active and recommendable.

For secure and reliable search operation, the proposed system is designed based on attributes and matching of infrequent repeating and similarity matching. These attributes are secure and have higher rate of analysis and formulating a meaningful data search. The main objective is clearly highlighted, and priority based mechanism provides double layer of security.

V. MODULATIRY DESCRIPTION AND IMPLEMENTATION RESULTS

The designed articles are made according to the flowing modulators which includes, Environment Programming and Configuration Registration and Network synchronization Unit, Friend Request and Friend List management System, Attribute alignment and search optimization Monitoring and Event generation.

(3)

________________________________________________________________________________________________

Fig 3: Administrative Support

The OSN currently has an inactive admin under monitoring and data driven social media is now facing a problem of synchronizations and thus we have realigned and managed to have higher privilege of OSN, a snap of privilege diagram of Admin is demonstrated in Fig 3.

Here the Admin is active and can create a event, page and vacancies for user. The user privacy is protected where user can set their profile is private or public mode.

VI. CONCLUSION AND OBSERVATIONS

The proposed system is now analyzed and designed, the privacy policy is appended and hence the above discussed system simulation is achieved. Under this process the following observations are noticed, the system is well achieved with updated policy for friend recommendation and thus the objective of the project is achieved.

REFERENCES

[1] P. W. L. Fong, M. Anwar, and Z. Zhao, “A privacy preservation model for facebook-style social network systems,” in Proc. 14^th Eur. Conf.

Res. Comput. Security, 2009, pp. 303–320.

[2] B. Carminati, E. Ferrari, and A. Perego,

“Enforcing access control in web-based social networks,” ACM Trans. Inf. Syst. Security, vol.

13, no. 1, pp. 6:1–6:38, Nov. 2009.

[3] A.C. Squicciarini, M. Shehab, and F. Paci,

“Collective privacy management in social

networks,” in Proc. 18th Int. Conf. World Wide Web, 2009, pp. 521–530.

[4] Squicciarini, F. Paci, and S. Sundareswaran,

“PriMa: A comprehensive approach to privacy protection in social network sites,” Ann.

Telecommunication., vol. 69, nos. 1/2, pp. 21–36, 2014.

[5] Mislove, B. Viswanath, K. P. Gummadi, and P.

Druschel, “You are who you know: Inferring user profiles in online social networks,” in Proc. 3rd ACM Int. Conf. Web Search Data Mining, 2010, pp. 251–260.

[6] Jøsang, R. Ismail, and C. Boyd, “A survey of trust and reputation systems for online service provision,” Decision Support Syst., vol. 43, pp.

618–644, Mar. 2007.

[7] J.-H. Cho, A. Swami, and I.-R. Chen, “A survey on trust management for mobile ad hoc networks,” IEEE Commun. Survey Tutorials, vol.

13, no. 4, pp. 562–583, Dec. 2011.

[8] K. Govindan and P. Mohapatra, “Trust computations and trust dynamics in mobile ad hoc networks: A survey,” IEEE Commun. Survey Tutorials, vol. 14, no. 2, pp. 279–298, Dec. 2012.

[9] W. Sherchan, S. Nepal, and C. Paris, “A survey of trust in social networks,” ACM Comput.

Survey, vol. 45, no. 4, pp. 47:1–47:33, Aug.

2013.

[10] R. Guha, R. Kumar, P. Raghavan, and A.

Tomkins, “Propagation of trust and distrust,” in Proc. 13th Int. Conf. World Wide Web, 2004, pp.

403–412.

[11] R. Dey, C. Tang, K. Ross, and N. Saxena,

“Estimating age privacy leakage in online social networks,” in Proc. IEEE Conf. Comput.

Commun., 2012, pp. 2836–2840.

[12] M. von Arb, M. Bader, M. Kuhn, and R.

Wattenhofer, “Veneta: Serverless friend-of-friend detection in mobile social networking,” in Proc.

IEEE Int. Conf. Wireless Mobile Comput. Netw.

Commun., Oct. 2008, pp. 184–189.

