Privacy-preserving distributed collaborative filtering

In order to provide accurate and dependable recommendations, online vendors need to have adequate data; however, due to the nature of online shopping and increasing amount of e-commerce sites, data collected for collaborative filtering purposes might be distributed among various companies, even competing ones. Those online vendors holding distributed data might want to offer predictions based on integrated data collaboratively. However, concerns regarding protecting private data, financial fears due to revealing valuable assets, and legal regulations imposed by various organizations prevent them from alliance.In this dissertation, various solutions are proposed to enable online vendors? collaboration for estimating recommendations on vertically or horizontally distributed data while preserving their confidentiality. The proposed solutions mainly employ randomized and cryptographic techniques for protecting privacy. To improve online performance, which may become worse due to collaboration, preprocessing methods such as clustering, dimensionality reduction, and trust are utilized. The recommended methods are analyzed in terms of privacy. Also, superfluous loads caused by privacy concerns are examined. Finally, real data-based trials are performed for evaluating the proposed schemes in terms of the quality of predictions. The analyses and experimental outcomes demonstrate that the methods preserve confidentiality, cause insignificant overheads, and offer accurate recommendations.