Product recommendation based on sybil and trusted votes in social networks

Abstract

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.