Trust aware system for social networks: A comprehensive survey

Social networks are the platform for the users to get connected with other social network users based on their interest and life styles. Existing social networks have millions of users and the data generated by them are huge and it is difficult to differentiate the real users and the fake users. Hence a trust worthy system is recommended for differentiating the real and fake users. Social networking enables users to send friend requests, upload photos and tag their friends and even suggest them the web links based on the interest of the users. The friends recommended, the photos tagged and web links suggested may be a malware or an untrusted activity. Users on social networks are authorised by providing the personal data. This personal raw data is available to all other users online and there is no protection or methods to secure this data from unknown users. Hence to provide a trustworthy system and to enable real users activities a review on different methods to achieve trustworthy social networking systems are examined in this paper

ATFR: Attribute based Trustworthy Friend Recommendations Online

Users today relay on social networking platforms for infor-mation retrieval. Information shared on the social networks will be cir-culated through their friend sharing them on their pro les. Thus making trustworthy friends in social networks is more important to maintain data privacy. Existing system compute friends based on their mutual friends which would not be e cient way of selecting friends online [1]. This allows user to get connected with strangers without any information of real friend or fake friend. To overcome this problem attribute based trustworthy friend recommendation system is proposed. Based on this, the attributes of each user is collected during pro le creation and these attributes are subjected to similarity between the users. Each user online are evaluated with their trust score with the help of Trust Score algo-rithm. Experimental result shows the number of friends recommended are trustworthy

Product Recommendation based on Sybil and Trusted Votes in Social Networks

Social Networks is a platform which is easily accessible by normal users worldwide. Online Social Networks facilitates users online to get registered with ease of speed and create their own accounts to communicate with the social world for information gathering. This platform allows everyone to get registered online irrespective of their social behaviour. Users here are creating duplicate accounts that is creating Sybil in the network. By this Sybil online Social Networks are suffering for different kinds of Sybil attacks online. In social networks user’s feedback and preferences play an important role in suggesting friends online or recommending products online. When collecting the feedback or preferences of any product online both Sybil user’s and real user’s data is considered as we are not differentiating the Sybil user or real user. From this products, recommended online will not have an efficient rating which would divert the buyers online. To over this problem we propose Sybil Community Detection Algorithm (SCD) and TrustRank Algorithm that bifurcates real user votes and Sybil users votes to fetch the efficient products online thus build secure online environment

Bifurcation of votes on Online Social Networks based on Sybil and Trusted Users

Social media platform is easily accessible by all users irrespective of their location. Online users can get registered on Social Networks with ease to create accounts and communicate with other users for information sharing. Due to this availability users are creating multiple duplicate accounts that results in sybils on network. Social networks are updated with the help of its users feedback and preferences in recommending online products. The process of collecting of feedback or preferences online for the products both sybil's and real user's information is collected. Sybil votes makes Product ranked high than they actually deserve. As any of the platforms have not taken any of the initiatives to find difference between sybil and real user feedback, hence to overcome this problem we have proposed an Sybil Community Detection Algorithm (SCD) to differentiate real user and sybil user feedback or vote thus build secure