Privacy-preserving profile matching system for trust-aware personalized user recommendations in social networks

Abstract

Trust is one of the important points to be considered regarding the security of social networks. In the proposed system, a framework that handles trust in social network is introduced, this uses the reputation mechanism. The reputation mechanism differentiates the implicit and explicit connections that exist in the network members. The semantics and dynamics of these connections are analyzed, and personalized user recommendations to other users of network are provided. Using the semantics of trust, recommendations will be provided by the system considering both the positive trust and negative trust between users. Along with this, the proposed system matches profiles of the users under consideration. The profile matching is used in reputation ratings calculated for suggestions of friends. For computing the reputation of each member, the properties of trust such as transitivity, personalization, and context are adopted by the proposed system. In social networks, trust cannot be perfectly transitive and also it decreases along the transition path, but people can communicate the trust. The aim of this work is to design a web-based recommender system in social network that will provide suggestions to the users by analyzing the behaviour of each user in the social network as well as filtering out the similar users from the network.