NAÏVE BAYESIAN CLASSIFIER-BASED PRIVATE RECOMMENDATIONS
PRIVACY-PRESERVING NAÏVE BAYESIAN CLASSIFIER-BASED COLLABORATIVE FILTERING
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Collaborative filtering (CF) has become very popular on the Internet. Although CF systems are widely used, they have various challenges in recommendation process. For better results, such systems need quality data; however, due to privacy concerns, users hesitate to send their private data or they might send false data. CF systems provide referrals on existing databases compromised of ratings recorded from groups of people evaluating various items; sometimes, the systems ratings might be split among different parties. The parties may wish to share their data; but they may not want to disclose ...
Collaborative filtering (CF) has become very popular on the Internet. Although CF systems are widely used, they have various challenges in recommendation process. For better results, such systems need quality data; however, due to privacy concerns, users hesitate to send their private data or they might send false data. CF systems provide referrals on existing databases compromised of ratings recorded from groups of people evaluating various items; sometimes, the systems ratings might be split among different parties. The parties may wish to share their data; but they may not want to disclose their data. Online computation time increases with augmenting number of users. In this book, approaches are proposed to overcome challenges for naïve Bayesian classifier (NBC)-based CF algorithm. A new scheme is proposed to produce NBC-based recommendations while preserving users privacy by utilizing randomized response techniques (RRT). To offer CF services on distributed data between two parties without violating their privacy, solutions are provided. And finally, a method is proposed for optimizing privacy-preserving NBC-based CF scheme using k-modes clustering.
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