Privacy-Preserving and Secure Recommender System Enhance with K-NN and Social Tagging

Abstract

With the introduction of Web 2.0, there has been an extreme increase in the popularity of social bookmarking systems and folksonomies. In this paper, our motive is to develop a recommender system that is based on user assigned tags and content present on web pages. Although the tag recommendations in social tagging systems can be very accurate and personalized, there exists an issue of risk to the privacy of user's profile, since the social tags are given by a user expose his preferences to other users in contact. To overcome this problem, we have incorporated obfuscation privacy strategies with the well-known Delicious dataset in social tagging based recommender system. We have applied the popular supervised machine-learning algorithm, K-Nearest Neighbours classifier to the dataset that recommends relevant tags to the user. Privacy has been introduced in our tag-based recommender system by hiding some of the necessary tags, bookmarks of a user and replacing them with some random tags and bookmarks. Our experiment results indicate that the recommender system being implemented is highly efficient in terms recall and privacy measure for different values of k. The results and comparisons indicate that we have successfully employed an effective tag recommender system, which also protects the user's privacy without any significant fall in the quality of recommendation.