Email this article Improved Privacy Preserving Profile Matching in Online Social Networks
Abstract
Social networking  became popular because of its digital communication technologies tools for extending the social circle of people. Privacy preservation became a significant issue in social networking. This work discussed user profile matching with privacy preservation and introduced a group of   profile matching protocols. Online social network with a mixture of public and private user profiles to predict the private attributes of users. We map this problem to a relational classification problem and we propose practical models that use friendship and group membership information (which is often not hidden) to infer sensitive attributes. The key novel idea is that in addition to friendship links, groups can be carriers of significant information. To the best of our knowledge, this is the first work that uses operation-based and group-based classification to study privacy implications in social networks with mixed public and private user profiles.
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